Applied Science, Faculty of
Materials Engineering, Department of
DSpace
UBCV
Varghese Ayrookuzhy, Ebbie
Master of Applied Science - MASc
University of British Columbia
Recent advancements in additive manufacturing have highlighted the limitations of conventional beam motion techniques in manipulating cooling rates and melt pool dynamics. This pursuit has led to the investigation of alternative approaches including multi-beam motion, beam oscillation, beam shaping and beam pulsing, all aimed at refining control over the manufacturing process. Notably, recent research emphasizes the potential benefits of operating in a keyhole regime during laser powder bed additive manufacturing. The primary focus of this research is to provide preliminary insights for our industrial partner (CANMORA TECH Inc.) on the feasibility of beam pulsing as a mechanism for controlling microstructure and particularly defects when melting under conditions that lead to keyholing. The aim is to achieve this control by dynamically adjusting the beam's position over time and modulating power within different duty cycles and frequencies in Laser Powder Bed Additive Manufacturing. The laser tracks are built under these varied pulsing conditions and the results of heat transfer modelling will be presented to predict the impact of pulsing on melt pool geometry.
The effect of beam pulsing on laser beam processing of Ti-6Al-4V using a prototype laser additive manufacturing platform by Ebbie Varghese Ayrookuzhy B.Tech. (Metallurgy), Mahatma Gandhi University, 2018 M.Sc. (Material Science and Engineering), University of Sheffield, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Materials Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2024 © Ebbie Varghese Ayrookuzhy, 2024 ii The following individuals certify that they have read and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: The effect of beam pulsing on laser beam processing of Ti-6Al-4V using a prototype laser additive manufacturing platform submitted by Ebbie Varghese Ayrookuzhy in partial fulfillment of the requirements for the degree of Master of Applied Science in Materials Engineering Examining Committee: Dr. Chad W Sinclair, Professor, Materials Engineering, UBC Supervisor Dr. Daan Maijer, Professor, Materials Engineering, UBC Supervisory Committee Member Dr Adam Thomas Clare, Professor, Mechanical Engineering, UBC Supervisory Committee Member iii Abstract Recent advancements in additive manufacturing have highlighted the limitations of conventional beam motion techniques in manipulating cooling rates and melt pool dynamics. This pursuit has led to the investigation of alternative approaches including multi-beam motion, beam oscillation, beam shaping and beam pulsing, all aimed at refining control over the manufacturing process. Notably, recent research emphasizes the potential benefits of operating in a keyhole regime during laser powder bed additive manufacturing, The primary focus of this research is to provide preliminary insights for our industrial partner (CANMORA TECH Inc.) on the feasibility of beam pulsing as a mechanism for controlling microstructure and particularly defects when melting under conditions that lead to keyholing. The aim is to achieve this control by dynamically adjusting the beam's position over time and modulating power within different duty cycles and frequencies in Laser Powder Bed Additive Manufacturing. The laser tracks are built under these varied pulsing conditions and the results of heat transfer modelling will be presented to predict the impact of pulsing on melt pool geometry. iv Lay Summary This thesis undertakes a comprehensive investigation into the effects of laser beam pulsing on a prototype commercial laser powder bed fusion system. The research focuses on characterizing and potentially predicting experiments conducted under keyholing on Ti-6Al-4V. The study delves into the potential impacts of laser beam pulsing on parameters such as heat input, melt track geometry and defect generation. By elucidating these dynamics, this work contributes to advancing our understanding of additive manufacturing processes, thereby informing the optimization of industrial practices and materials development. v Preface This thesis, titled " The effect of beam pulsing on laser beam processing of Ti-6Al-4V using a prototype laser additive manufacturing platform", documents the research conducted by Ebbie Varghese Ayrookuzhy during the Master of Applied Science program at UBC Materials Engineering under the guidance of Dr. Chad Sinclair. The melt tracks studied in this thesis were prepared by CANMORA TECH Inc. All of the microstructural characterization of the samples was performed by the author. The micro-CT experiments were performed in the Department of Chemical Engineering at UBC and the raw data was processed, segmented and analysed by the author. The results from FAST modelling were performed by the author and Dr. Chad Sinclair while the CFD simulations were performed by Dr. Chad Sinclair. All analysis of the model results were performed by the author including interpretation and comparison with experiments. vi Table of Contents Abstract ....................................................................................................................................................... iii Lay Summary ............................................................................................................................................. iv Preface .......................................................................................................................................................... v Table of Contents ....................................................................................................................................... vi List of Tables ............................................................................................................................................. vii List of Figures ........................................................................................................................................... viii List of Symbols .......................................................................................................................................... xii Acknowledgements ................................................................................................................................... xiii Chapter 1 - Introduction ............................................................................................................................ 1 Chapter 2 – Literature Review .................................................................................................................. 3 2.1 Basics of metal additive manufacturing .......................................................................................... 3 2.2 Laser-material interaction: Mechanisms and control of melt pool formation ............................ 9 2.3 Advanced beam control techniques ............................................................................................... 20 2.3.1 Beam shaping ............................................................................................................................ 21 2.3.2 Multi-laser beam systems ........................................................................................................ 25 2.3.3 Fastbeam motion and Beam oscillation .................................................................................. 27 2.3.4 Laser Pulsing ............................................................................................................................ 29 Chapter 3 – Scope and Objectives ........................................................................................................... 33 Chapter 4 – Methodology ......................................................................................................................... 34 4.1 Materials and Methods ................................................................................................................... 34 4.2 Microstructural Characterization ................................................................................................. 35 4.3 Porosity measurements ................................................................................................................... 36 4.4 Computational Modelling ............................................................................................................... 37 Chapter 5 – Experimental Results ........................................................................................................... 42 5.1 Measurements of Melt Track Geometry from Surface Measurements ...................................... 42 5.1.1 Melt Track Observations ......................................................................................................... 43 5.1.2 Measurements of the Melt Track Width ................................................................................ 49 5.1.3 Surface roughness .................................................................................................................... 53 5.1.4 Melt track morphology – Cross section .................................................................................. 60 5.2 Micro-computed Tomography Observations ............................................................................... 63 5.3 Summary .......................................................................................................................................... 78 Chapter 6 – Simulations to understand melt pool dimensions and porosity formation ..................... 79 6.1 Rationalising the observation of keyholing ................................................................................... 79 6.2 Prediction of melt pool dimensions using FAST model ............................................................... 80 6.2.1 Prediction of melt pool dimensions using FAST model: Continuous melt track................ 80 6.2.2 Prediction of melt pool dimensions using FAST model: Pulsed melt track ........................ 84 6.2.3 Integrating FAST model with phenomenological model for melt pool depth predictions 89 6.3 Prediction of melt pool size and porosity formation using CFD modelling ............................... 92 6.3.1 Prediction of melt pool dimensions using CFD model for continuous melt tracks ............ 93 6.4 Summary ........................................................................................................................................ 100 Chapter 7 – Conclusion .......................................................................................................................... 101 7.1 Future scope ................................................................................................................................... 102 References ................................................................................................................................................ 103 vii List of Tables Table 1:Spectral absorption of different materials under different lasers [32]. .............................................. 5 Table 2: Material properties used in the FAST model. Values taken from those used in reference [151]. .. 38 Table 3: Material properties used in CFD model. Values taken from those used in reference [153]. .......... 40 Table 4: The various combinations of process parameters used in the samples. .......................................... 43 Table 5: Compilation table of melt pool geometry and aspect ratio for continuous and various pulsed conditions for both solid and powder melt tracks. ........................................................................................ 60 viii List of Figures Figure 1: Schematic diagram of LPBF process [13] and interior of a LPBF machine [15]. ........................... 3 Figure 2:The major process factors that affect the LBPF process [40]. .......................................................... 5 Figure 3: Surface morphologies of single melt tracks of SS316L under varying power and scan speed conditions. The zones indicate different types of melt tracks: zone A shows no melting, zone B displays discontinuous melt tracks, zone C shows continuous but unstable melt tracks and zone D exhibits continuous and stable melt tracks [44]. ........................................................................................................... 7 Figure 4: Various scanning strategies used in SLM [48]. ............................................................................... 8 Figure 5: Forces that govern the melt pool dynamics [59]. .......................................................................... 10 Figure 6: Impact of Surface Tension Temperature Coefficient on the melt pool [59]. ................................. 11 Figure 7: (Left) General trend for variation of the mode depending on the power density. (Right) Melt pool formation mechanism in the three different types of melting mode a) conduction b) transition c) keyhole mode [67]. ..................................................................................................................................................... 12 Figure 8: Melt pool profiles at various power and scan speed combinations illustrating the three melting modes – conduction (a), transition (b) and keyhole (c-e) [44]. ..................................................................... 13 Figure 9: Cross-sectional view of a single melt track illustrating the geometry of the melt pool [44] and variation in melt pool geometry as conditions approach keyholing (a2) from conduction mode (a1) [68]. . 14 Figure 10: Stages of melt pool and vapour depression evolution in Ti-6Al-4V using high-speed synchrotron x-ray imaging [69]. ....................................................................................................................................... 15 Figure 11: Power-Velocity process map for understanding defects in Ti-6Al-4V [84] ................................ 17 Figure 12: Common defects in LPBF process [90,91]. ................................................................................. 17 Figure 13: a) Influence of aspect ratio on pore formation for a constant keyhole depth. b) A sequence from the CFD model illustrating the keyhole collapse, solidification and porosity formation [92]. .................... 19 Figure 14: Top hat intensity profile [117]. .................................................................................................... 22 Figure 15: a) Cross sections view of melt tracks scanned by top hat and gaussian shaped lasers under different powers. (b) Experiment and (d) simulation of a melt track produced by a 700 W top-hat shaped laser. (c) Experiment and (e) simulation of a melt track produced by a 350 W Gaussian beam shaped laser [108]. ............................................................................................................................................................. 22 Figure 16: Gaussian and doughnut beam profile and impact of it on the melt pool geometry [117]. .......... 23 Figure 17:Schematic representation of Gaussian, Ring and Bessel beams with contour plots of the laser intensity profiles. Top-down (a) and side view (b) showing the grain microstructure [118]. ....................... 24 Figure 18:Impact of Gaussian and Elliptical beam on the microstructure and grains [97]. .......................... 25 Figure 19: (Left) Multi-laser beam system [125] and (right) schematic showing multi-laser system a) Preheating b) Post-heating beams [122]. ...................................................................................................... 26 Figure 20:a) Schematic of the spot scan algorithm. b) Application of algorithm to minimise solidification cooling rate in region 1 and maximise it in region 2 of the Mona Lisa. c) Magnified view of melt locations. d) Final solidification cooling rate distribution. e) Grain morphology of the two regions [132]. ................ 28 Figure 21:A schematic diagram illustrating the flow of the melt pool and microstructure formation in Ti–43Al-9V-0.5Y alloy for circular beam oscillation with low oscillation velocity. Microstructure of the non-oscillating (a-c) and oscillating (d-f) samples under different velocities [134]. ........................................... 29 Figure 22: Temporal profile for pulsing. ...................................................................................................... 30 Figure 23: Pore formation in Al6061 under varying laser frequencies (4,7 &10kHz) at a constant duty cycle (50%DC) for a laser power of 470 W and a scan speed of 400 mm/s [96]. .................................................. 31 Figure 24: Weld microstructure using continuous wave laser with a) Nitrogen shielding gas showing interface cracking b) Argon shielding gas showing entrapped porosity c) Weld microstructure using pulsed laser welding [144]. ...................................................................................................................................... 32 Figure 25: Section of the base plate showing melt tracks at different frequencies for 50% duty cycle, illustrating the steady-state portion and transient portion of the melt track. The time and distance travelled by the beam during the on/off transitions for each frequency. ..................................................................... 35 Figure 26:Keyence VHX-7000 ..................................................................................................................... 36 Figure 27:Zeiss Xradia Versa 520 system. ................................................................................................... 37 Figure 28: The temperature-dependent material properties (specific heat and thermal conductivity) of Ti–ix 6Al–4V used in the FAST model [151]. ....................................................................................................... 38 Figure 29: The domain size used in the CFD model. .................................................................................... 41 Figure 30: Ti-6Al-4V Base plate with the solid melt tracks (labelled 1-25) on the left side and powder melt tracks (labelled 1-25) on the right side. The corresponding pulse frequencies and duty cycles are mentioned on the left and last row respectively. ............................................................................................................. 42 Figure 31:Continuous solid melt tracks for various powers a) 500W, b) 375W, c) 250W & d) 125W as viewed by the Keyence. ................................................................................................................................ 44 Figure 32: Steady state continuous solid melt tracks observed at higher magnification compared to Fig 29 for four power levels ranging from 125W to 500W. .................................................................................... 44 Figure 33:Continuous powder melt tracks for various powers a) 500W, b) 375W, c) 250W & d) 125W as viewed by the Keyence. ................................................................................................................................ 44 Figure 34:Steady state continuous powder melt tracks at higher magnification compared to Fig 31 for four power levels ranging from 125W to 500W. .................................................................................................. 45 Figure 35: Steady-state solid melt tracks are shown for various pulsing frequencies (ranging from 10 kHz to 10 Hz) across three different duty cycles (25%, 50%, and 75%). Smaller magnification images are provided at lower frequencies (50 Hz and 10 Hz) to indicate the regions from which the higher magnification images (red box) were captured. ............................................................................................ 46 Figure 36: Steady-state powder melt tracks are shown for various pulsing frequencies (ranging from 10 kHz to 10 Hz) across three different duty cycles (25%, 50%, and 75%). Smaller magnification images are provided at lower frequencies (50 Hz and 10 Hz) to indicate the regions from which the higher magnification images (red box) were captured. ............................................................................................ 48 Figure 37: Illustrating various irregularities in pulsed powder melt tracks: a) 5kHz-50% DC showing denudation and necking formation, b) 5kHz-75% DC showing bulging or ‘balling’ and c) 50Hz-50% DC showing discontinuous melt tracks. .............................................................................................................. 49 Figure 38: Methodology for measuring experimental melt track width. ...................................................... 49 Figure 39: Variation in melt pool widths for continuous (CW) solid melt tracks (round dots) and powder (hollow rectangles) melt tracks as a function of power. Powder melt tracks are prefaced by ‘P’ while ‘S’ for melt tracks made on the solid base plate. ...................................................................................................... 50 Figure 40: Variation in melt pool widths of steady-state solid melt tracks across different pulse frequencies at (a) 25% duty cycle, (b) 50% duty cycle and (c) 75% duty cycle. ............................................................. 51 Figure 41: Magnified sections of Fig 40 (b) highlighting the differences in melt pool widths between (a) intermediate frequencies and continuous 250W power and (b) high frequencies and continuous 250W power. ........................................................................................................................................................... 52 Figure 42: Variation in melt pool widths of steady-state powder melt tracks across different pulse frequencies at (a) 25% duty cycle, (b) 50% duty cycle and (c) 75% duty cycle. The number in the brackets represents the mean and standard deviation for each pulsing condition in the powder melt tracks. ............ 53 Figure 43:Variation of surface roughness of continuous solid melt tracks for various powers a) 125W b) 250W c) 375W & d) 500W. .......................................................................................................................... 54 Figure 44: Variation of surface roughness of continuous powder melt tracks for various powers a) 125W b) 250W c) 375W & d) 500W. .......................................................................................................................... 55 Figure 45: Variation of surface roughness of solid melt tracks for different pulsing frequencies at 50% duty cycle a) 10kHz-50% duty cycle, b) 5kHz-50% duty cycle, c) 1kHz-50% duty cycle, d) 500Hz-50% duty cycle & e) 100Hz-50% duty cycle ................................................................................................................ 56 Figure 46:Variation of surface roughness of solid melt tracks for (a) 25% duty cycle, (b) 50% duty cycle and (c) 75% duty cycle at a constant frequency of 500Hz. ........................................................................... 57 Figure 47: Variation of surface roughness of powder melt tracks for different pulsing frequencies at 50% duty cycle a) 10kHz-50% duty cycle, b) 5kHz-50% duty cycle, c) 1kHz-50% duty cycle, d) 500Hz-50% duty cycle & e) 100Hz-50% duty cycle. ....................................................................................................... 58 Figure 48:Variation of surface roughness of powder melt tracks for different duty cycles at a constant frequency of 500Hz and 100Hz. a) 500Hz-25% DC, b) 500Hz-50% DC, c) 500Hz-75% DC, d) 100Hz-25% DC, e) 100Hz-50% DC & f) 100Hz-75% DC. ..................................................................................... 59 Figure 49:Solid melt pool cross-section for various pulsing frequencies at different duty cycles. Continuous solid melt track cross-section is given at top for reference. .......................................................................... 61 Figure 50: Powder melt pool cross-section for various pulsing frequencies at different duty cycles. x Continuous powder melt track cross-section is given at top for reference. .................................................. 62 Figure 51: Volume fraction estimation method in continuous a) 250W - solid and b) 250W - powder melt tracks. ............................................................................................................................................................ 64 Figure 52: Spatial profile of the porosity a) the top view b) side view. The yellow box in the top corner indicates the region of study for porosity in this 250W continuous solid melt track. The x-axis indicates the width of the melt track, y-axis - the depth of the melt track and the z-axis- the length of the melt track. .... 65 Figure 53: Number of pores a) per unit length b) per unit depth in 250W continuous solid melt track. ...... 66 Figure 54: a) Number of pores per aspect ratio b) 2D representation of aspect ratio as a function of length and depth for the 250W continuous solid melt track. The dotted line represents the melt pool depth as measured by the Keyence microscope. ......................................................................................................... 66 Figure 55: a) Cross section of the 250W solid melt track showing variation of feret diameter b) Number of pores per feret diameter ................................................................................................................................ 67 Figure 56: Spatial profile of the porosity a) the top view b) side view. The yellow box in the top corner indicates the region of study for porosity in this 250W continuous powder melt track................................ 68 Figure 57: Number of pores a) per unit length b) per unit depth in the 250W continuous powder melt track. The dotted line marks the separation between the bead and the base plate and is considered as the origin. 68 Figure 58: a) Number of pores per aspect ratio b) 2D representation of aspect ratio as a function of length and depth for the 250W continuous powder melt track. The first dotted line marks the separation between the powder and the base plate and is considered as the origin while the second dotted line represents the melt pool depth as measured by the Keyence microscope. ........................................................................... 69 Figure 59: a) Cross section of the 250W powder melt track showing variation of feret diameter b) Number of pores per feret diameter. ........................................................................................................................... 70 Figure 60: Spatial profile of the porosity a) the top view b) side view. The yellow box in the top corner indicates the region of study for porosity in this 500Hz & 50%DC solid melt track. .................................. 71 Figure 61: Number of pores a) per unit length and b) per unit depth for the 500Hz & 50%DC solid melt track. ............................................................................................................................................................. 71 Figure 62: a) Number of pores per aspect ratio b) 2D representation of aspect ratio as a function of length and depth for the pulsed 500Hz & 50%DC solid melt track. ........................................................................ 72 Figure 63: a) Cross section of the 500Hz & 50%DC pulsed solid melt track showing variation of feret diameter b) Number of pores per feret diameter. .......................................................................................... 73 Figure 64: Spatial profile of the porosity a) the top view b) side view. The yellow box in the top corner indicates the region of study for porosity in this 500Hz & 50%DC powder melt track. .............................. 74 Figure 65:Number of pores a) per unit length and b) per unit depth for 500Hz & 50%DC powder melt track. ............................................................................................................................................................. 75 Figure 66: Cumulative number of pores across 500Hz & 50%DC solid and powder melt track. The solid melt track is represented by blue and the green represents the powder melt track. The steps represent the occurrence of each pulse, occurring at intervals of 0.1 mm. ......................................................................... 76 Figure 67: Cumulative number of pores across pulsed 500Hz & 50% DC and continuous 250W solid melt tracks. The pulsed melt track is shown in blue and the continuous 250W melt track is represented in green. ...................................................................................................................................................................... 76 Figure 68: a) Number of pores per aspect ratio and b) 2D representation of aspect ratio as a function of length and depth pulsed 500Hz & 50%DC powder melt track. The dotted line marks the separation between the powder and the base plate and is considered as the origin. ....................................................... 77 Figure 69:a) Cross section of the 500Hz & 50%DC pulsed powder melt track showing variation of feret diameter b) Number of pores per feret diameter. .......................................................................................... 78 Figure 70: The melt pool geometry created using results from FAST model superimposed on the experimental cross sections for various power levels. .................................................................................. 80 Figure 71: Comparison of melt pool width variations predicted by the FAST model (FM) against experimental (EXP) results for continuous melt tracks in solid Ti-6Al-4V across various power levels and absorption efficiencies of 0.4, 0.45 and 0.5. The error bar (black) represents the maximum predicted power loss of up to 10%, indicating an actual output range between 450 to 500W. ............................................... 81 Figure 72: Comparison of melt pool width variations predicted by the FAST model against experimental results (black) across various power levels and absorption efficiencies (η) of 0.4, 0.45 and 0.5 for various beam size. The darker symbols (circles) correspond to the beam standard deviation of 145µm. ................. 82 xi Figure 73: Comparison of melt pool width variations predicted by the FAST model across various power levels for various beam standard deviation (100-200µm) for no radiation condition and for different emissivity (ϵ) values. ..................................................................................................................................... 83 Figure 74: Comparison of the melt pool width predictions from FAST model superimposed onto the experimental cross sections of the top surface for selected pulsing conditions a) 10Hz-50%DC b) 10Hz-75%DC c) 100Hz-25%DC d) 100Hz-50%DC & e) 100Hz-75%DC. ........................................................... 84 Figure 75:(a-c) Variation in melt pool width predicted from average beam power calculations and experimental for three different frequencies (100Hz,500Hz and 1000Hz) across three different duty cycles (a) 25%, (b) 50% and (c) 75%. The blue solid line represents the predicted melt pool width based on the average power. (d-f) Variation in melt pool width predicted by the FAST model and experimental for three different frequencies (100Hz,500Hz and 1000Hz) across three different duty cycles (d) 25%, (e) 50% and (f) 75%. The dashed lines correspond to the average continuous power according to the duty cycle (black) and the continuous 500W melt track (red). The blue shows the average power delivered to the material at a point. ............................................................................................................................................................. 86 Figure 76: Variation of aspect ratio as a function of the power across an absorption efficiency of 0.45 for various beam standard deviation (100-200µm). The darker symbols ( green circles) correspond to the beam standard deviation of 145µm. The black circles indicate the results from experiments. The error bar (black) represents the maximum predicted power loss of up to 10%, indicating an actual output range between 450 to 500W. ........................................................................................................................................................ 91 Figure 77: a) Keyhole aspect ratio as a function of keyhole number for various materials and processing conditions showing a unified trend despite variations in material properties and processing parameters [73]. b) Aspect ratio is plotted as a function of keyhole number for Ti-6Al-4V under various experimental conditions with different beam standard deviations and power, exhibits the same trend and collapses into a single curve. .................................................................................................................................................. 92 Figure 78: The melt pool geometry created using results from CFD model superimposed on the experimental cross sections for various power levels. .................................................................................. 93 Figure 79: a) Variation of melt pool width as a function of power for experimental, FAST and CFD models b) Variation of melt pool depth as a function of power for experimental, FAST, empirical approach and CFD models. The error bar (black) represents the maximum predicted power loss of up to 10%, indicating an actual output range between 450 to 500W. .............................................................................................. 94 Figure 80: A single time frame illustrating melt pool shape and formation of keyhole in a continuous (a) 250W, (b) 375W and (c) 500W melt track. The white line represents the boundary between the gas and liquid phases while the blue line separates the solid from the liquid. ........................................................... 95 Figure 81: Melt pool evolution and the formation of gas bubbles in a continuous 375W melt track. The white line represents the boundary between the gas and liquid phases while the blue line separates the solid from the liquid. .............................................................................................................................................. 96 Figure 82: Melt pool evolution and the formation of porosity in pulsed 500Hz and 50%DC melt track. The blue line represents the boundary between the gas and liquid phases while the white line separates the solid from the liquid. .............................................................................................................................................. 97 Figure 83: Illustration of porosity formation in keyholing conditions during laser welding. The red region represents the melt pool while the blue region depicts the solidified weld containing entrapped porosity. Figure redrawn and adapted from [183]. ...................................................................................................... 98 Figure 84: Sequential stages of laser pulsing in LPBF of Ti-6Al-4V, illustrating porosity formation [96]. 99 xii List of Symbols η Laser absorption efficiency P Power (W) Vs Scan speed (mm/s) h Hatch spacing (mm) d Layer thickness (mm) λ Wavelength (mm) ρ Density (kg/m3) P Power (W) Cp Specific Heat (J/kg.K) σ Beam standard deviation T Temperature (K) k Thermal conductivity (W/m/K) t Time (s) Tl Liquidus temperature (K) T0 Initial temperature (K) α Thermal diffusivity (m2/s) r0 Beam radius (mm) Ke Keyhole number e* Aspect ratio xiii Acknowledgements At the outset, let me, in all humility, thank God Almighty for providing me this incredible opportunity to study in a prestigious university like the University of British Columbia and to be able to interact and learn from eminent scholars of this remarkable institution. I consider opportunities as rare chances to develop and reach one’s potential and in my case, I am hugely blessed to have been guided and provided with opportunities to work in additive manufacturing by none other than Dr. Chad Sinclair. I am immensely indebted to him for his unstinted support and consistent motivation during the process of this project. Dr Sinclair’s invaluable advice and expertise has truly enlightened my path to the successful completion of this thesis. It would be a dereliction of my duty if I fail to acknowledge and express my gratitude to Dr. Daan Maijer and Dr. Adam Thomas Clare who have played an immense role in shaping this thesis as my supervisory committee members. I would also like to thank, Mr. Ralf Edinger and Dr. Will Sparling from CANMORA TECH Inc. and Mr. Shaun Cooke for their unerring sense of direction from the start to the end of my project is evidenced through the much-needed data and samples they had provided me with, and which has certainly augmented this project. In the face of challenges, their timely and precise suggestions have made all the difference. My sincere gratitude is also due to Ms. Heli Eunike and Mr. Sabyasachi Roy who had graciously assisted me in completing the experiments. I also wish to take this opportunity to express my wholehearted appreciation and thanks to all my friends and colleagues of the University of British Columbia as well as to my adviser Ms Sonia William for their careful consideration and discerning advice, all of which has contributed into the making of my thesis a reality. Last but not the least, I am forever indebted to my loving parents, my sister and friends whose sacrifices and indomitable encouragement has spurred me on to pursue my interests in the field of material science and dream big. They are the wind beneath my wings, and I dedicate this thesis to them. Ebbie Varghese 1 Chapter 1 - Introduction Additive Manufacturing (AM), also known as 3D printing has ushered in a new era of manufacturing by introducing novel possibilities several industries. AM encompasses a variety of techniques that build objects layer by layer directly from digital designs [1]. It enables the fabrication of intricate, customised and geometrically complex structures [2]. This manufacturing technology has found applications across industries from aerospace and automotive [3,4] to healthcare and consumer goods [5,6]. AM can be classified into seven major categories according to the ASTM standards [7] based on the underlying processes and techniques used to create objects layer by layer. Selective Laser Melting (SLM) is a variant of Laser Powder Bed Fusion (LPBF) AM that originated in 1995 at the Fraunhofer Institute ILT in Aachen, Germany [8]. This thesis specifically will focus on the effects of laser pulsing on melt pool formation in Ti-6Al-4V samples. Melt tracks both in solid material and in powder will be examined. Laser pulsing is one of several techniques that has been proposed as a method for improving AM production by giving additional process flexibility to control heat input. The existing literature presents a mixed perspective regarding the advantages of laser pulsing in additive manufacturing. Some studies suggest it leads to improved microstructures and material properties while others report increased porosity as a result of pulsing. Chapter 2 of this thesis provides a comprehensive literature review on the PBF process. It is divided into three subsections. The first subsection explains the fundamentals of the LPBF process, exploring the major process parameters that govern the process and its impact on the melt track morphology. It also discusses the advantages and challenges associated when using this process. The second subsection delves into the role of fluid dynamics such as Marangoni flow, recoil pressure and buoyancy in shaping the melt pool morphology. Key concepts such as aspect ratio, keyhole number and the three modes of melting: conduction, transition and keyhole are examined. Additionally, power-velocity (PV) process maps are discussed alongside various types of defects observed in Selective Laser Melting (SLM) across multiple studies. The third subsection focuses on advanced beam control techniques such as beam shaping, multi laser beams and beam oscillation techniques currently employed in the market with particular emphasis to laser pulsing, which is the core focus of this thesis. The scope and objectives of the thesis are outlined in Chapter 3 while Chapter 4 outlines the methodology and procedures used to experimentally investigate the melt pool morphology of SLM-printed parts. 2 Chapter 5 presents the experimental results with measurements of melt tracks using Keyence microscopy from both top and cross-sectional views. A detailed analysis of the melt pool dimensions including both width and depth as well as the surface roughness of the top side is also conducted. Additionally, it offers an in-depth discussion of porosity findings obtained through Micro-CT scans. Finally, Chapter 6 builds from the experimental results in Chapter 5 by using two types of numerical models (a thermal only model and a fluid dynamics-based model) to help develop a better understanding of the results from Chapter 5 and the limitations of the modelling techniques. The goal is to determine whether these simulations can accurately predict melt pool geometry under the same conditions without the need for physical experiments. Finally, the thesis is concluded by a Conclusions and Future work discussion in Chapter 7. 3 Chapter 2 – Literature Review 2.1 Basics of metal additive manufacturing Powder Bed Fusion (PBF) is an advanced Metal Additive Manufacturing (AM) technology in which an energy source is employed to selectively fuse or melt powdered particles, layer by layer to construct objects with the desired geometries [5,9,10]. PBF processes are primarily classified into two types based on the energy source: Laser Powder Bed Fusion (LPBF) [11] and Electron beam Powder Bed Fusion (EPBF) [12]. This thesis centres on the former. The LPBF AM process also known as Selective Laser Melting (SLM) employs a laser beam as an energy source to selectively melt and fuse metal powders to form a three-dimensional object [11]. The setup as shown in Fig 1 consists of a laser, build platform, a control unit and powder delivery system [13]. A substrate, usually a metal plate, is fixed onto the build platform which serves as the base for layer deposition. An initial layer of metal powder is spread evenly across the build platform using a recoater blade or roller. A computer controlled high intensity laser beam delivers thermal energy to the powder bed surface melting the powder in accordance with the current layer of the 3D model. This layer-by-layer approach continues with the build platform being lowered after each layer is completed, fresh powder is spread and laser melts the powder to build up the object. The laser's movement and focus are controlled by a beam deflection system comprising of galvanometer mirrors and a focusing lens [14]. Figure 1: Schematic diagram of LPBF process [13] and interior of a LPBF machine [15]. Inert gases serve dual functions in the LPBF process. First, they create a protective atmosphere to prevent oxidation of the metal powder [16]. Secondly, the inert gas stream is directed across the powder bed to clear away spatter and metal vapour produced during laser melting. These by-products are then directed into the 4 machine’s filters through a vacuum system. Effective removal of these by-products is essential to prevent their redeposition on the powder bed which can cause defects in subsequent layers and affect the overall build quality [17]. The powder bed is usually preheated in the EPBF process while in the LPBF process, preheating is less common [15,18,19]. In LPBF, heating can be used for certain materials to promote bonding between layers and to reduce residual stresses in the material [19]. This helps improve the mechanical properties of the final part and reduce distortion [20]. However, in EPBF, preheating is primarily aimed at preventing "smoking", a phenomenon where electrostatic charges accumulate on the powder particles due to the electron beam. This charge accumulation can cause the particles to repel each other and be ejected from the build area leading to defects and material loss [18]. Preheating mitigates this problem by increasing electrical conductivity. Post processing methods such as heat treatment and machining are often required to achieve the desired properties and to improve the surface roughness of the final product [21]. The type of laser used impacts the melt pool geometry and porosity by affecting energy absorption and heat distribution within the powder bed [22,23]. Neodymium-doped Yttrium Aluminium Garnet (Nd:YAG) and carbon dioxide (CO2) lasers are two commonly used lasers in LPBF. The former one operates at shorter wavelengths, around 1064 nanometres, which is in the near-infrared region of the electromagnetic spectrum. It results in higher absorptivity for most metals at equivalent scanning speeds while the latter one operates at longer wavelengths around 10.6 micrometres, which is in the far-infrared region of the electromagnetic spectrum [24,25]. Various studies have highlighted the critical role of laser wavelength in the absorption rate of certain non-ferrous materials such as copper and aluminium [26,27]. Processing high-reflectivity materials like pure copper poses challenges due to their high reflectivity and thermal conductivity making it difficult to achieve high-density parts (relative density above 95%) through SLM. Short wavelength lasers are found to be particularly advantageous in this regard. Recent advancements in AM have introduced lasers like blue lasers [28,29] operating at wavelengths between 400–480 nm and green lasers [30,31] around 500–560 nm tailored to enhance the absorption rate for such materials. For instance, copper exhibits absorption rates around 54.5% for green lasers and 65.2% for blue lasers while aluminium shows around 13.5% absorption rate across both green and blue lasers. Spisz [32] et al. have detailed spectral absorption data for copper and aluminium across various laser wavelengths confirming that metals like copper have higher absorption rates with shorter wavelengths as shown in Table 1. Recently, ytterbium (Yb)-doped fibre lasers have emerged offering advantages such as high electrical-to-optical efficiency, compact system design and excellent beam quality over other lasers [33]. As a result, these lasers produce more stable and slightly wider scan tracks while maintaining the speed and high build quality resulting in uniform scan tracks with low or no porosity [34,35]. They typically operate at a wavelength of 1070 5 nanometres. This wavelength is close to that of the Nd:YAG laser, placing it in the near-infrared region of the electromagnetic spectrum. Also, the remote guidance of laser beams through optical fibre cables eliminates the necessity for complex optic systems [36]. Table 1:Spectral absorption of different materials under different lasers [32]. Wavelength (nm) Spectral absorption (%) Copper Stainless Steel Aluminium Titanium Blue laser - 457 65.2 43.2 13.6 80.8 Green laser - 525 54.5 40.2 13.5 78.6 IR laser - 1085 4 30.1 5.2 65.2 Optimising the process parameters is another major concern when it comes to the LPBF process. A lot of research in LPBF surrounds investigating the effects of various process parameters as shown in Fig 2 such as power, scan speed, layer thickness, hatch parameters and powder properties [37,38,39]. Figure 2:The major process factors that affect the LBPF process [40]. The laser power is a metric that quantifies the energy input into the material per unit time. If the power is high, it will cause keyholing and even lead to defects such as porosity. Insufficient power will lead to undermelting and defects like lack of fusion. Keyholing is the formation of a deep, narrow cavity in the material caused by intense laser energy allowing the laser beam to penetrate deeply through vaporized material. Beuth et al [39] has demonstrated that increasing the laser power enlarges the melt pool dimensions and induces high residual stresses in the material. Scan speed determines the rate at which the laser beam moves across the powder 6 bed surface. It controls the duration the powder bed is exposed to the laser. If certain parameters such as power, layer thickness and spot size are kept constant, increasing scan speed can increase the build rate [41]. However, this can also lead to defects such as undermelting, balling, and lack of fusion [42]. Low scan speed increases the amount of energy delivered to the material over a given area, which can raise the temperature within the powder bed due to the prolonged exposure to the heat source. This can result in keyholing, induces high residual stresses and increases the melt pool dimensions [43]. In Fig 3, the surface morphologies of single melt tracks of SS316L change under varying power and scan speed conditions [44]. These variations can be categorised into four zones, each indicating a different type of melt track. In zone A, the low power and high scan speed result in a very minimal energy input, which is insufficient to penetrate into the base plate. This zone shows an absence of melt tracks. In Zone B, the energy input is insufficient to produce a consistent melt pool leading to discontinuous melt tracks. The surface morphology in this zone features isolated patches or segments of melted material. Zone C displays continuous melt tracks that exhibit instability with irregularities in the melt track width, height or surface smoothness. This indicates that while the energy is sufficient for melting, the process parameters are not optimised for stability. Finally, Zone D shows melt tracks that are both continuous and stable with a smooth and consistent surface morphology. This reflects an optimal combination of power and scan speed ensuring consistent and reliable melting [44]. The typical powder layer thickness in LPBF is usually between 20-60µm [45]. For any industry, time and cost are critical factors in the manufacturing process. Increasing the powder layer thickness can expedite production and improve cost efficiency. However, this approach negatively impacts the quality and resolution of the final components particularly in terms of surface roughness [46]. Surface roughness is a critical factor for fatigue performance. The rougher the surface, the lower the fatigue life. This is because rough surfaces have more stress concentrators which can initiate cracks and lead to premature failure under cyclic loading conditions [47]. Thicker layers can cause incomplete melting as the laser may not fully penetrate and melt the entire layer resulting in defects such as lack of fusion. This issue is particularly pronounced with reflective materials like copper, which may not efficiently absorb laser energy in thicker layers [26]. Hatching and contouring are two different techniques used to melt the powder bed and build up the layers of a part. Hatching involves filling the interior of each layer with parallel or criss-crossing lines, where the laser beam moves back and forth in a pattern like unidirectional, bidirectional or island scanning to create the bulk material. Contouring traces the outer edges of each layer before or after hatching with the laser beam following the part’s perimeter to ensure precise dimensions and a better surface finish. The hatch parameters dictate how the laser beam traverses within each layer and how it varies between successive layers. They primarily include the hatch distance and the 7 hatching/scan strategy. The hatch distance is the space between adjacent laser beam paths with studies indicating that increasing this distance can lead to higher porosity in the specimens [40,48]. The scan strategy defines the pattern in which the laser moves across the build platform. So, there are different ways in which the laser can be moved such as uni-directional, bi-directional as shown in Fig 4, resulting in components with different mechanical properties and microstructure. Figure 3: Surface morphologies of single melt tracks of SS316L under varying power and scan speed conditions. The zones indicate different types of melt tracks: zone A shows no melting, zone B displays discontinuous melt tracks, zone C shows continuous but unstable melt tracks and zone D exhibits continuous and stable melt tracks [44]. Kruth et al [49] found that using island scanning methods with different checkerboard sizes in Ti-6Al-4V lowered residual stress in the material. Wan et al [50] studied how different scanning strategies affect the mechanical properties of SLM manufactured IN718. They utilised bi-directional scanning with and without a 90° rotation. Specimens without rotation exhibited higher tensile and fatigue strength than those scanned with a 90° rotation. Additionally, varying the number of passes (single or multiple) in powder melting methods significantly influences the mechanical and microstructural properties of components. Increasing the number of 8 passes promotes finer microstructures that increase strength and reduces porosity by helping trapped gases escape in the final parts [51]. Figure 4: Various scanning strategies used in SLM [48]. The LPBF process offers significant advantages such as the ability to create complex shapes that traditional manufacturing methods such as casting often struggle with, enabling intricate geometries and the consolidation of multiple parts into a single assembly [52]. This capability not only simplifies assembly processes but also reduces weight, cost and potential points of failure in products. Another key benefit is rapid prototyping, which accelerates product development cycles by allowing quick iteration and testing of designs [53]. It helps in identifying potential issues and making necessary improvements without the delays associated with traditional manufacturing methods. However, LPBF comes with its own challenges. Initial setup costs are quite high due to the need for high precision equipment. LPBF parts often require postprocessing to achieve desired surface finish or mechanical properties, adding time and cost to production [54]. Material selection is another consideration. While the range of materials available is expanding, it is still more limited compared to traditional 9 manufacturing processes like machining or casting. LPBF processes are also prone to build variability [55]. Factors like powder characteristics, process parameters and environmental conditions can lead to variations in part quality and performance between build runs [56]. This variability contributes to higher rejection rates and requires careful process control and quality assurance measures. Variations between runs may require adjustments to process parameters to achieve consistent results. High rejection rates due to part defects or inconsistencies can impact production efficiency and overall cost-effectiveness [55]. Despite these challenges, AM offers complex geometric design flexibility, rapid iteration capabilities and the potential for significant reductions in production lead times and costs when properly implemented and optimised [57]. 2.2 Laser-material interaction: Mechanisms and control of melt pool formation When a laser beam strikes the surface of a material, several interconnected processes occur. Initially, the material absorbs the laser energy. This absorption depends on factors such as the material's properties and the wavelength of the laser. The absorbed energy is converted into heat leading to an increase in the material's temperature. As the material continues to absorb laser energy and heat up, it eventually reaches its melting point. At this stage, the material undergoes a phase transition from solid to liquid, forming what is known as a melt pool. The size and depth of the melt pool depend on the process parameters such as power, scan speed and the thermal properties of the material. Volumetric energy density (VED) and linear energy density (LED) are two parameters that researchers use to quantify the melt pool geometry [58]. VED measures the amount of energy delivered per unit volume of material. It helps determine how much energy is absorbed throughout the material's volume as it heats up and melts. Higher VED means more energy per volume resulting in larger and deeper melt pools. LED measures the energy delivered per unit length along the laser's path, affecting how uniformly and consistently the material heats up and melts along the laser's path. They are calculated using the equations 𝐿𝐸𝐷 =𝑃𝑉𝑠 𝑉𝐸𝐷 =𝑃𝑉𝑠. ℎ. 𝑑 where, P is the laser power (J/s), 𝑉𝑠 is the scan speed (mm/s), h denotes hatch spacing (mm) and d is the layer thickness (mm). Materials with high thermal conductivity will dissipate heat more quickly affecting the melt pool geometry. The melt pool dynamics are governed by various forces, making it essential to understand their impact on fluid flow within the melt pool. These forces include Marangoni forces, buoyancy and recoil pressure, each playing a significant role in the shape and stability of the melt pool. Figure 5 illustrates the forces that govern the melt pool dynamics during laser welding or AM process [59]. 10 Figure 5: Forces that govern the melt pool dynamics [59]. Marangoni forces arise due to temperature and/or composition dependent variations in surface tension within the melt pool. As the laser heats the material, the centre of the melt pool becomes hotter. For a material whose surface tension decreases with increasing temperature this results in lower surface tension in the center compared to the cooler edges. This creates a surface tension gradient and induces flow from the centre to the periphery of the melt pool. However, this flow direction can also reverse depending on the surface tension temperature coefficient. This flow is known as Marangoni convection or thermocapillary convection. When the surface tension temperature coefficient 𝑑𝛾𝑑𝑇< 0, the surface tension gradient drives the molten material to flow from the hot centre towards the cooler edges creating an outward convective flow as shown in Fig 6 [59]. This widening effect near the surface is characteristic of the Marangoni effect influencing the melt pool shape. If the surface tension temperature coefficient were positive 𝑑𝛾𝑑𝑇> 0, Marangoni convection would work in the opposite direction. The molten material would flow inward from the cooler edges to the hotter centre resulting in a deeper but narrower melt pool. Higher energy input increases the melt pool size and top surface temperature leading to strong evaporation of light elements such as aluminium in Ti-6Al-4V. As aluminium evaporates, it reduces the overall aluminium content in the melt pool which can change the liquid's density and viscosity leading to changes in Marangoni flow patterns [60]. Wang et al [61] found that variations in the sulphur content of steels impact the surface tension gradient and affects Marangoni flow in the molten melt pool. It caused different motions due to variations in chemical composition and altered the microstructure and mechanical properties. 11 Figure 6: Impact of Surface Tension Temperature Coefficient on the melt pool [59]. Buoyancy effects in the melt pool are driven by temperature induced density variations. As the laser heats the material, a melt pool is created by localised melting. Due to the temperature gradient within the melt pool, hotter regions are less dense and tend to rise while cooler and denser regions sink. This movement sets up convection currents within the melt pool facilitating the redistribution of heat and molten material impacting the melt pool geometry and reducing porosity. Srinivasan et al. [62] demonstrated through numerical modelling that the effects of buoyancy are negligible compared to Marangoni convection in laser melting. This finding is supported by experimental studies conducted by Martin et al [63]. In the LPBF process, there are usually three modes of melting - conduction mode, transition mode and keyholing mode (Fig 7). Assuncao et al. [64] have demonstrated that the transition from conduction to keyhole mode is influenced not only by the power density but also by the beam diameter and interaction time. They also found that the penetration is higher for higher interaction time and lower beam diameter at the same power density. A vapour depression can exist in all three melting modes. Therefore, the laser beam generally does not interact directly with a flat melt pool but rather some form of vapour depression, which can be shallow or deep [65]. The presence of vapour depression affects how the laser energy is absorbed and distributed within the melt pool. The degree to which the material is vaporised determines the transition points between conduction, transition, and keyhole melting modes. At lower power densities, the material primarily melts due to heat conduction with minimal vaporisation. Eager et al [66] found that in conduction mode, the melt pool is usually shallow and semicircular in shape and the melt pool depth (MPD) is usually less or half the melt pool width (MPW). 12 Figure 7: (Left) General trend for variation of the mode depending on the power density. (Right) Melt pool formation mechanism in the three different types of melting mode a) conduction b) transition c) keyhole mode [67]. As the power density increases, vaporisation begins to occur more significantly creating deeper and more pronounced vapour depressions within the melt pool as shown in Fig 8(b) [44]. This is known as transition mode. It represents an intermediate state where both conduction and vaporisation effects are notable. The melt pool is mostly stable but the shape of the melt pool tends to be conical and deep in nature [68]. At higher power densities, Fig 8(c-e), the temperature of the material can exceed its boiling point causing parts of the material to vaporise i.e., the vaporisation becomes much more intense. This vaporisation generates a vapour plume over the melt pool which can impact the stability and quality of the melt pool by introducing turbulence and affecting the consistency of the laser-surface interaction. In keyhole mode, the depth of the molten pool is primarily influenced by the recoil pressure. Recoil pressure occurs when the energy input from the laser causes some material to vaporise generating a vapour plume above the melt pool. This vapour exerts a downward force on the molten surface potentially creating depressions and increasing the depth of the melt pool. Recoil pressure can also induce spattering where droplets of molten material are ejected from the melt pool affecting the surface quality and uniformity of the final part [69]. It is characterised by the formation of a vapour cavity or "keyhole" within the melt pool leading to a much deeper and narrower melt pool. However, in some studies, the melt pools have been observed to exhibit a bimodal shape with a bowl on top and a spike at the bottom [68,70]. Convective heat transfer driven by Marangoni flow is the dominant heat transfer mechanism within the melt pool [70]. The upper region of the melt pool takes on an hourglass shape and is usually wide due to the radial action of the Marangoni flow. The bottom part tends to be narrow, resembling a conical keyhole. Also, the melt pool depth in this mode is greater than half the melt pool width. The laser absorption dynamics undergo significant changes as it progresses to a keyhole. Initially when the laser beam falls on the metal surface, most of it gets reflected and a small melt pool is formed. With increased laser irradiance, the surface temperature rises causing vaporisation and forming a vapour depression due to the recoil pressure from the evaporating material [71]. 13 Figure 8: Melt pool profiles at various power and scan speed combinations illustrating the three melting modes – conduction (a), transition (b) and keyhole (c-e) [44]. This depression is counteracted by surface tension that tries to minimise the surface energy by narrowing the opening. As the keyhole forms, the laser beam penetrates deeper into the material increasing the surface area for absorption. Multiple reflections occur within the keyhole as laser rays bounce off the internal walls of the cavity, enhancing the absorption of laser energy. These internal reflections concentrate the laser energy deeper within the material resulting in the formation of a deeper and more stable keyhole [72]. Gan et al [73] has developed universal scaling laws for keyhole stability and porosity formation. They are essential because they provide a consistent framework for understanding and controlling the AM process across different materials and machines. Manufacturers can achieve uniform quality and reduce defects ensuring low 14 variability between builds. The behaviour and stability of the keyhole during the laser-metal interaction process are usually quantified by parameters such as aspect ratio and keyhole number [74]. The scaling law is considered universal because by normalizing data across various laser power levels, scan speeds, spot sizes and even different materials using this dimensionless parameter, all the data points align on a single curve when plotted. Despite the variations in process parameters, the relationship between the keyhole aspect ratio and the keyhole parameter (Ke) remains consistent. This indicates that the dynamics governing stability of a keyhole and formation of porosity are fundamentally similar across different conditions making the scaling law a powerful tool for predicting and controlling the melt pool geometry. The aspect ratio (e*) is defined as the melt pool depth divided by the melt pool width as shown in Fig 9. It is a metric used to classify the melting modes into conduction, transition, and keyhole modes [74]. Figure 9: Cross-sectional view of a single melt track illustrating the geometry of the melt pool [44] and variation in melt pool geometry as conditions approach keyholing (a2) from conduction mode (a1) [68]. Cunningham et al. [69] used high-speed synchrotron x-ray imaging to study the evolution of the melt pool and vapour depression in a Ti-6Al-4V bare plate as shown in Fig 10. The process began with the initial formation of a melt pool when the laser irradiates on the surface (A), followed by the formation of a small, stable vapour depression within the melt pool (B). This depression grew steadily (C) but developed instabilities over time (D). These instabilities caused rapid changes in the vapour depression's shape (E and F), leading to periodic fluctuations (G and H). Eventually, the melt pool's shape changed from a quasi-semicircular form to a bimodal structure with a bowl on top and a spike at the bottom (I and J). They found that the aspect ratio of the melt pool increased over time with two distinct transitions. The first transition called the vapour depression transition occurred with the onset of fluctuations in the vapour depression, which causes an increase in the aspect ratio. The second transition, the melt pool transition happened when the aspect ratio approached approximately 0.5. After this second transition, the rate of increase in the aspect ratio slowed [69]. 15 Figure 10: Stages of melt pool and vapour depression evolution in Ti-6Al-4V using high-speed synchrotron x-ray imaging [69]. Gan et al [73] observed that the aspect ratio follows the universal scaling law only with a single dimensionless parameter known as the Keyhole number (Ke). 𝐾𝑒 =𝜂𝑃(𝑇𝑙 − 𝑇0)𝜋𝜌𝐶𝑝√𝛼𝑉𝑠𝑟03 where η is absorptivity, P is the laser power, 𝑇𝑙 is the liquidus temperature, 𝑇0 is the substrate temperature, ρ denotes density, 𝐶𝑝 denotes heat capacity, α is the thermal diffusivity, 𝑉𝑠 is the scan speed 𝑉𝑠 and the beam radius is denoted by ro. The relationship between the aspect ratio and the keyhole number is given by 𝑒∗ = 0.4(𝐾𝑒 − 1.4) They quantified the three different melting modes using e* and Ke. • Conduction mode (e* = 0 or Ke < 1.4) • Transition mode (e* ≤ 2 or 1.4 ≤ Ke ≤ 6) • Keyhole mode (e* > 2 or Ke > 6) This type of classification provides a simple and effective way to identify the onset of the keyhole and to reduce defects. Conduction mode is traditionally linked to low laser power density [75]. In some cases, the mode is defined when the aspect ratio is smaller than 0.5 [69,76]. Despite differing definitions, it is widely agreed that conduction mode occurs when material vaporisation is minimal or when the power density is too low to induce boiling [77,78]. One of the challenges when operating in conduction mode is the limited range of process parameters to work with, resulting in shallow penetration depth [79]. To achieve greater penetration depth and operational flexibility, transitioning to transition or keyhole mode becomes essential. Another significant drawback of conduction mode is its low coupling efficiency, especially with highly reflective materials. Coupling efficiency refers to the percentage of laser energy absorbed by the material [80]. Keyhole mode offers concentrated heat input resulting in a smaller heat-affected zone (HAZ) and minimising thermal 16 distortion [81]. This mode is advantageous for highly reflective materials like copper and aluminium which pose challenges in conduction mode due to their low absorption rates. Keyhole mode significantly enhances energy absorption efficiency by absorbing most of the laser energy. Nath et al. [82] found that the absorption rate for stainless steel can reach up to 65% in keyhole mode compared to just 15% in conduction mode which leads to more effective heating and melting of the material. Power-Velocity (PV) process maps serve as essential tools for visualising how process parameters impact specific features of interest (Fig 11). They demonstrate the effects of different combinations of laser power and scanning speed on factors such as part density, defects and microstructure. For instance, Montgomery et al [83] demonstrated how PV process maps could be used to control melt pool geometry in SLM-manufactured IN625. They generated linear curves that indicate a consistent relationship between the laser power and scan speed. This means that for any given point on the curve, if you know the power, you can determine the corresponding scan speed that will produce a specific melt pool geometry. This enables the prediction and adjustment of melt pool dimensions by altering the power and velocity settings. Luo et al [84] utilised process maps to identify the correlation between processing parameters and the mechanical properties in LPBF manufactured Ti-6Al-4V. They developed these process maps by experimentally assessing the impact of laser power and scan speed on microstructure, defect formation and mechanical properties. These maps help to determine the optimal processing conditions for achieving dense builds or identifying defects, thereby enabling the customization of material properties. Similarly, Gordon et al. [85] demonstrated the application of processing maps in understanding defects in LPBF. They established boundaries to distinguish between lack-of-fusion (LOF) and keyholing defects using micro-tomography analysis results [86]. Soylemez et al [44] established a process map for SLM manufactured Ti-6Al-4V to identify the defects and changes in surface morphologies of the melt track for various combinations of power and scan speed. Several studies have been made to understand defect formation and their evolution in these modes [85,87,88]. Some of the common defects are balling, lack-of-fusion, cracks and porosities as shown in Fig 12 [87]. Lack of fusion defects arise from insufficient overlap between successive laser passes or layers typically due to low energy input. This results in unmolten or partially melted powder as the melt pool does not penetrate deeply enough to fuse with adjacent material. Increasing laser power, decreasing scanning speed and hatch spacing are some of effective strategies to mitigate these defects [88]. Balling defects occur due to instability in the melt pool often caused by high surface tension that forces the molten metal to contract into spherical droplets rather than forming a continuous track. Mitigation strategies include lowering laser power, reducing scanning speed and improving substrate wetting through pre-heating 17 [89]. Keyhole porosities form when excessive laser power causes deep melt pool penetration and vaporisation creating a vapour-filled cavity that can collapse and trap gas bubbles. This leads to spherical or irregularly shaped pores within the material [69]. Examining the surface of the melt tracks offers a simple way to identify some of these defects as they are dependent on the melt pool geometry [90]. Figure 11: Power-Velocity process map for understanding defects in Ti-6Al-4V [84] Figure 12: Common defects in LPBF process [90,91]. 18 Zhou et al. [92] studied the dependence of porosity formations on keyhole aspect ratios. They found that small aspect ratio keyholes which are relatively wide and shallow, facilitate quick and efficient backfilling of liquid metal which prevents porosity. However, the medium and large aspect ratio keyholes are deeper relative to their width leading to more gas entrapment and incomplete filling which results in porosity. The increased depth of these keyholes prolongs the backfilling time, making it difficult to completely fill the keyhole and thus increasing the risk of porosity formation. Figure 13 (b) illustrates a typical keyhole collapse and solidification process simulated using Computational Fluid Dynamics (CFD) model for pulsed laser welding [92]. Once the laser is turned off, the temperature drops rapidly causing the keyhole wall temperature to decrease quickly especially in the lower part where a thin layer of liquid metal is present. This leads to the rapid solidification of the liquid metal at the bottom. As the temperature drops, the liquid metal at the top of the keyhole flows inward and downward under hydrostatic pressure and surface tension filling the keyhole from the top first. The downward flow of liquid metal slows due to friction from the wall and the inward movement of the solid-liquid interface. As the keyhole refills, the liquid region shrinks particularly at the bottom due to higher conduction heat loss. Eventually, the bottom part of the liquid metal solidifies before reaching the keyhole's bottom leaving a pore at the root. As shown in Fig 11, the true utility of PV processing maps lies in their ability to identify and predict the occurrence of porosity due to lack-of-fusion, balling and entrapped gas based on the chosen processing parameters. By examining the contours on a PV map, one can pinpoint optimal laser power and scanning speed combinations that minimise these defects and maximise part quality. Understanding the melt pool dynamics in keyhole mode through experiments alone is challenging, so numerical simulations have been employed to illustrate the physical phenomenon and defects associated with keyhole-mode. Tang et al. have explained how the melt pool flows during keyhole-mode using a computational fluid dynamics (CFD) model [93]. They observed intense and unsteady molten flow around the keyhole and the vortex formation at the rear of the melt pool contributes to bubble formation leading to pores. Tan et al. described how spherical and elliptical pore shapes are formed using numerical and CFD models [94]. Any entrapped vapour or gas near the bottom of the collapsing keyhole gets trapped and takes on spherical or elliptical shapes due to surface tension. Shrestha et al [95] found that collapse of a keyhole during laser melting is dependent on the balance between surface tension pressure and evaporation pressure. When the laser power is low, the energy input is insufficient to maintain a stable keyhole causing it to collapse as the surface tension pressure exceeds the evaporation pressure. At higher laser power levels, the evaporation pressure becomes dominant and the keyhole expands allowing for deeper penetration. Their study on the power speed effect shows that laser power significantly influences keyhole porosity due to its impact on keyhole stability. For the same linear energy density, the number and volume of pores increase with raising power up to a certain level and then decrease with further power increase. This is 19 due to the stabilization of keyhole formation at different laser energy intensities [95]. In LPBF processes, operating in a stable keyhole regime is garnering attention to achieve high-density parts. Traditionally, operating in keyhole mode is considered detrimental due to its potential to increase porosity formation [92,96]. However, recent insights from Rollet et al. [70] suggest that operating in a stable keyhole regime offers several benefits over operating in conduction mode. One of the key benefits of operating in this regime is its increased energy efficiency. This is because energy absorption increases significantly when the laser melting transitions from conduction mode to keyhole mode. The laser beam undergoes multiple reflections within the keyhole leading to increased laser absorption, especially in highly reflective metals like aluminium and copper. Simonds et al. [72] also pointed out that absorption decreases as the vapour depression depth reduces. Figure 13: a) Influence of aspect ratio on pore formation for a constant keyhole depth. b) A sequence from the CFD model illustrating the keyhole collapse, solidification and porosity formation [92]. 20 This results in a more sustainable and energy efficient process, expanding the process window far beyond what is achievable in conduction mode. Secondly, operating in this regime significantly reduces lack-of-fusion porosity, a common defect that occurs when powder particles are not fully melted. The thermocapillary force within the keyhole attracts and captures nearby pores effectively removing them from the build area resulting in high quality builds with fewer defects [96]. Thirdly, the stable keyhole maintains consistent melt pool morphology and depth, even in the case of fluctuations in laser and powder conditions which increases the reliability of the process [69]. Finally, it improves the mechanical and corrosion properties of the part [97]. Huang et al. [98] showed that operating in a stable keyhole mode can potentially refine the grain structure, increase material strength and decrease the likelihood of cracking. However, an unstable keyhole regime may result in keyhole porosity and a reduction in fatigue life. 2.3 Advanced beam control techniques Most of the SLM process primarily relies on lasers with a Gaussian beam shape [99]. However, the distinctive energy distribution of Gaussian beams characterised by high intensity at the centre and lower intensity at the periphery often induces substantial thermal gradients and destabilised the melt pool. This can lead to undesirable defects and coarse grains in the manufactured parts [100]. Adjusting process parameters such as power, scanning speed and layer thickness is a common approach to mitigate these issues but it has its limitations [101,102]. Beyond certain thresholds, these adjustments can cause problems like overheating, spatter formation, balling and lack of fusion. This occurs because fine-tuning these parameters alone cannot always ensure optimal energy distribution or desired melt pool geometry [103]. To address these challenges, beam control techniques are introduced which help in even distribution of heat, reduce thermal gradients and enhance melt pool stability. Despite their benefits in automation and repeatability, commercial AM systems often face limitations in how the beam can be manoeuvred across the surface [99,104]. This restriction in beam movement can pose challenges in achieving precise control over microstructure, solidification kinetics and melt pool geometry. There is limited literature available on beam control techniques in SLM and their influence on melt pool geometry, microstructure and defect formation [44,105,106,107]. The choice of beam control technique significantly impacts the efficiency, microstructure and material properties of the manufactured parts. Some beam control techniques that have been reported in literature include beam shaping [108,109], multi-laser beam systems [105,110], fast beam motion/beam oscillation [106,111,112] and laser pulsing [113,114,115] which will be discussed further. 21 2.3.1 Beam shaping Beam shaping is a beam control technique that is used to get an even or uniform energy distribution ensuring thorough melting of the powder, control the melt pool geometry and microstructure [99]. As discussed earlier, most of the SLM process use a Gaussian beam shape. When a Gaussian shaped laser hits a surface, the energy distribution follows a bell-curve pattern with the highest intensity at the centre and gradually decreasing intensity toward the edges [100]. This means that the centre of the beam delivers significantly more energy to the material than the periphery creating substantial thermal gradients leading to melt pool instability and defects. Making simple alterations to beam shape can bring variations in the thermal behaviour, morphology and microstructure of the single tracks [100]. Some common beam shapes reported in the literature include top-hat intensity [108,116], doughnut profile [117], elliptical beam shapes, ring-shaped and Bessel beams [100]. Each of these shapes is engineered through various optical techniques from the standard Gaussian beam shape to influence melt pool characteristics differently. For instance, a top hat/flat top beam profile is typically achieved using beam shapers such as diffractive optical elements (DOEs). These DOEs reshape the Gaussian beam which has a peak intensity at the centre into a flat-top profile by redistributing the energy evenly across the beam's cross section as shown in Fig 14. This involves diffracting the incoming light to spread out the high central intensity, flattening the beam profile and eliminating the peak intensity. Consequently, when used in SLM, top-hat beams produce a uniform depth melt pool reducing the likelihood of defects such as balling and lack of fusion. Yuan et al. [108] investigated microstructures of IN718 produced from a flat-top beam with large spot size and with a Gaussian shaped laser. They showed that flat-top laser promoted the epitaxial growth of columnar grains. This is because the flat-top laser creates a melt pool with uniform depth and a flat bottom boundary as seen in Fig 15. This flat bottom boundary creates a more consistent temperature gradient across the melt pool which is crucial for the controlled growth of columnar grains [109]. Cheng et al [116] aimed to tailor microstructures by modifying the morphology of the melt pool and the solidification mechanism. They printed IN718 superalloy parts with top-hat and Gaussian intensity distributions. Parts created with the flat-top mode laser exhibited a uniform distribution of long columnar grains. In contrast, parts printed with the Gaussian laser mode displayed a different pattern featuring alternating regions with large columnar dendrites and regions with finer dendritic structures. 22 Figure 14: Top hat intensity profile [117]. Figure 15: a) Cross sections view of melt tracks scanned by top hat and gaussian shaped lasers under different powers. (b) Experiment and (d) simulation of a melt track produced by a 700 W top-hat shaped laser. (c) Experiment and (e) simulation of a melt track produced by a 350 W Gaussian beam shaped laser [108]. 23 Doughnut profile beams have a high-intensity ring with a low-intensity centre. DOEs phase-shift the incoming Gaussian beam to redistribute its energy into a ring-shaped pattern creating a doughnut-shaped beam with a dark centre and a bright outer ring. Wischeropp et al. [117] pointed out that the single tracks created with a donut shaped beam profile are wider and shallower compared to those produced with a Gaussian shaped beam profile. They also observed that the donut shaped beam profile resulted in fewer defects such as cracks and porosity as seen in Fig 16. Ring shaped and Bessel shaped beam profiles are quite similar to doughnut shaped beam profiles. These beam shapes are usually produced using axicons which are conical lenses designed to convert a Gaussian beam into a ring-shaped or Bessel beam. They achieve this by introducing a specific phase delay that causes the light to spread out in a way that forms a ring or a series of concentric rings. Ring-shaped beams have a hollow centre with a bright ring while Bessel beams have a central peak surrounded by concentric rings [118]. Figure 16: Gaussian and doughnut beam profile and impact of it on the melt pool geometry [117]. 24 Holla et al [119] found that ring-shaped beam profiles create broader and shallower melt pools. They help reduce spatter, porosity and create uniform melt tracks with better bonding between layers. As seen in Fig 17, Moore et al. [118] observed that Gaussian shaped beams lead to columnar grains extending across the entire width of the melt pool. However, a transition in grain morphology occurs in ring shaped beams, where columnar grains develop from the outer edges of the ring beam and the centre of the solidified region exhibits equiaxed grain structures. Bessel shaped beams produced shallow melt pools and small equiaxed grains. Both ring and Bessel-shaped beam profiles enable higher process windows for laser scan speeds and power while maintaining a stable melt pool. These profiles stabilise the LPBF process at high laser powers and scanning speeds, reducing defects and expanding the process window due to their larger diameters and lower intensities. This enhances process stability and accelerates the process. This is also supported by findings by Metel et al [120]. Tumkur et al [100] illustrated how Bessel shaped beams help in stabilising melt pool turbulence and extending solidification time. Figure 17:Schematic representation of Gaussian, Ring and Bessel beams with contour plots of the laser intensity profiles. Top-down (a) and side view (b) showing the grain microstructure [118]. 25 Elliptical beam shapes are characterised by their elongated form which provides a non-circular spot on the target. These beams are ideal for applications requiring different heating patterns along different axes. Elliptical beam shapes are generated by astigmatic optics which stretch the Gaussian beam along one axis and create elongated melt pools which are beneficial for specific directional solidification patterns [97]. Fig 18 shows how beam ellipticity significantly impacts the solidification microstructure [97,121]. This indicates that the morphology of the grains can be tailored by varying the beam intensity spatial profile while keeping process parameters constant. Figure 18:Impact of Gaussian and Elliptical beam on the microstructure and grains [97]. 2.3.2 Multi-laser beam systems Multi-laser beam systems involve the simultaneous use of multiple laser beams [105,122]. Instead of relying on a single laser source, these systems incorporate multiple laser sources each equipped with its beam delivery system. The key innovation is in synchronising and directing these laser beams to converge on different regions of the build platform (Fig 19). This synchronisation allows for precise and simultaneous melting of the metal powder, enabling faster production while maintaining the integrity of the final part [123]. Heeling et al. [122] conducted a study to investigate the impact of using multiple lasers for preheating and melting on the microstructure and melt pool geometry in the SLM manufactured SS316L. The positioning of the heating beam (wider spot size) in relation to the melting beam (smaller spot size) plays a critical role (Fig 19). When the heating beam precedes the melting beam, it facilitates preheating of the area around the melt pool and tends to yield smoother surfaces compared to the single beam ones. This approach reduces residual stresses, improves density and surface roughness [124]. 26 Figure 19: (Left) Multi-laser beam system [125] and (right) schematic showing multi-laser system a) Preheating b) Post-heating beams [122]. Conversely, if the heating beam follows the melting beam, it enables post-heating which serves to homogenise the temperature field behind the melt pool and reduce cooling rates [122,126]. Employing multi-beam strategies has a significant influence on melt pool geometry. For instance, in the preheating strategy, the heating beam precedes the melting beam raising the material's temperature before the melting beam irradiates it. This results in larger, half-spherical melt pools with bigger dimensions compared to single-beam due to the additional energy input from the heating beam. At low offsets, the heating beam overlaps with the melting beam or vice versa increasing the heat input and thereby increasing the melt pool dimensions [122]. Adani et al [127] reported that the number of operating laser beams significantly impacts spatter and inclusion formation. Xie et al. [128] investigated the impact of overlap region width (ORW) on defects. They found that as the ORW increases, porosity also rises significantly, ranging from 0.01% to 7.08% primarily due to the re-melting process. However, the increase in ORW also contributed to reducing microcrack size. This reduction is attributed to the smaller width-to-depth ratio of the melt pools and the formation of more cellular structures in the overlapping regions due to the re-melting process. Multi-laser systems offer substantial advantages, primarily in terms of reduced build times and reduced size of the heat-affected zone [126]. However, it is important to acknowledge the practical challenges associated with multi-laser systems. These systems demand intricate beam control and synchronisation mechanisms which increase both system complexity and cost [124]. 27 2.3.3 Fastbeam motion and Beam oscillation Fastbeam motion or fastbeam deflection is a technique that involves rapidly steering a laser or electron beam to create multiple melt pools simultaneously. This allows one to process multiple regions at the same time, increasing the overall production rate. In this process, the beam is deflected quickly either by using galvanometer mirrors in laser systems or electromagnetic lenses in electron beam systems. In the LPBF process, achieving fast beam motion with high precision is challenging because the galvanometer mirrors must be mechanically moved to steer the laser beam which limits the speed and precision of beam deflection [129]. However, EPBF systems utilise electromagnetic lenses to deflect the electron beam. The electric field within these lenses can be altered by adjusting the current allowing for rapid changes in beam direction [130]. So, theoretically it is easier to move the electron beam over large distances in a short time compared to lasers. Fastbeam motion operates on two different timescales: one timescale on which the entire part is being built and a much faster timescale for the beam's local motion. The beam is displaced very quickly across small or large distances allowing for the simultaneous processing of multiple regions. This dynamic control also helps in controlling the energy input in different and specific sites of the material [131]. While LPBF struggles with the precision required for fast beam motion due to the mechanical constraints of galvanometer mirrors, EPBF excels, making it more suitable for this advanced technique. Thus, fast beam motion is more common and effective in EPBF processes. Plotkowski et al [132] used an adaptive spot scan algorithm to demonstrate the ability to tailor grain morphology and crystallographic texture in the EPBF process for arbitrary cross-sections. The scan pattern algorithm for EPBF was based on a spot-melting concept to create a complex cross-section in the shape of the Mona Lisa. They controlled the grain structure of the IN718 by manipulating the spatial distribution of solidification conditions as seen in Fig 20. They achieved significant differences in the solidification cooling rates between two distinct geometric regions and were able to tailor site-specific microstructures. Another technique gaining traction is beam oscillation which involves moving the beam in a repetitive back-and-forth or circular pattern [133]. This movement helps to spread the energy more evenly across the melt pool. As a result, the solidification process becomes more uniform leading to reduced defects and improved material quality. Beam oscillation is particularly effective in achieving homogeneous microstructures and ensuring consistent mechanical performance [107]. Beam oscillation can mitigate issues like porosity, lack of fusion and uneven material distribution. It enhances part quality and can reduce the need for post processing [112]. Gao et al [134] studied the metallurgical defects and microstructure characteristics in LPBF manufactured Ti–43Al–9V-0.5Y alloy with non-oscillation and circular beam oscillation. Their study revealed that circular beam oscillation was more effective in eliminating pores and cracks in the alloy as seen in Fig 21. 28 Figure 20:a) Schematic of the spot scan algorithm. b) Application of algorithm to minimise solidification cooling rate in region 1 and maximise it in region 2 of the Mona Lisa. c) Magnified view of melt locations. d) Final solidification cooling rate distribution. e) Grain morphology of the two regions [132]. The reduction in pores is attributed to the circular beam oscillation which enhances keyhole stability, improves melt convection and provides more time for bubbles to escape. The beam’s stirring action promotes the growth of fine equiaxed grains and reduces the brittle TiAl phase, effectively preventing cracking. Kraetzsch et al [135] and Wang et al [136] have shown that wobbling the beam increases the melt pool size (in magnitudes of mm), extends the melt pool lifetime (10-100ms) and creates uniform temperature gradient by stirring the molten pool. This provides sufficient time for bubbles to escape, significantly reducing cooling rates and thermal stress. Hao et al. [137] found that varying the oscillation frequency from 20 to 1000 Hz in dissimilar steel systems affects the melt pool shape and melt track topology. When the frequency exceeds 200 Hz, the energy is spread out more evenly and quickly reduces the intensity and depth of the heat input. This transition changes the melt pool from a deep keyhole mode to a shallower heat conduction mode resulting in less concentrated energy input and shallower penetration which is particularly beneficial for thin-walled structures. 29 Figure 21:A schematic diagram illustrating the flow of the melt pool and microstructure formation in Ti–43Al-9V-0.5Y alloy for circular beam oscillation with low oscillation velocity. Microstructure of the non-oscillating (a-c) and oscillating (d-f) samples under different velocities [134]. 2.3.4 Laser Pulsing Laser pulsing is a technique where a laser emits energy in short, controlled bursts instead of a continuous beam [138]. Each pulse consists of a fixed amount of energy released over a specified duration. This method allows for precise control over the heat input into a material, reducing the risk of overheating and enabling more accurate and efficient material processing. It’s essential to understand a few basic concepts such as duty cycle, pulse frequency, pulse overlap and pulse duration to better understand how laser pulsing happens. The duty cycle represents the fraction of time during which the laser is on over the whole period. For example, a duty cycle of 50% indicates that the laser emits pulses for half of the cycle duration and remains off for the other half [113]. Pulse frequency or pulse repetition rate (PRR) indicates how often the laser pulses occur within a given time period. Higher pulse frequencies mean more pulses are delivered per second. The pulse frequency plays a significant role in how the energy is distributed across the material and can impact factors like melting efficiency and the formation of defects. 30 Figure 22: Temporal profile for pulsing. Pulse duration is the time interval during which each laser pulse is active. Shorter pulse durations concentrate energy into very brief intervals which allows more precise control over energy input. Longer pulse durations deliver more energy per pulse which can affect the depth and width of the melt pool [114]. Pulse overlap refers to the extent to which consecutive laser pulses overlap each other in the scan direction and between adjacent scan lines [139]. When pulses overlap significantly, the material receives a higher cumulative energy input which can lead to better fusion and densification. However, excessive overlap can cause overheating, increased spatter or defects such as cracking. Mumtaz et al. [138] demonstrated that pulsing helps minimise thermal distortions and enhance spatial resolution particularly for thin wall structures. This is achieved by delivering more precise heat input and controlling the formation of the melt pool throughout the process. Caprio et al [113] observed that laser pulsing tends to produce more defects and lower densification in the material compared to continuous waves. This is due to the intermittent nature of pulsing which results in less efficient heat transfer and more disturbance of the melt pool. Demir et al [114] have shown that by adjusting the pulse overlap, one could control the heat input into the material. This control is useful for intricate structures like lattices which are heat sensitive. Inadequate pulse overlap can cause uneven heating, leading to regions in the melt pool that are either excessively hot or too cool. This uneven heating can result in melt pool instability and create defects. Zhou et al [92] found that the porosity formed during the collapse and solidification of a keyhole are influenced by two factors: the solidification rate and the backfilling speed of the liquid metal. If the backfilling speed is too slow, pores or voids remain. By controlling the laser pulse shape can delay solidification, allowing more time for the liquid 31 metal to fill the keyhole and reduce porosity. Chen et al [140] found that using a ramp-down pulsed laser improved the cracking resistance of AA6061 by about 50% compared to a rectangular pulsed laser. This is due to the slower solidification rate and reduced thermal gradients in the melt pool. Hojjatzadeh et al. [96] investigated pore formation mechanisms in laser pulsing using in-situ high-speed synchrotron x-ray imaging. They focused on understanding how pores form in Al6061 alloy under varying laser frequencies and duty cycles. When the laser irradiation stops abruptly within a single pulse, the vapour depression in the melt pool collapses rapidly. This can trap gases leading to the formation of pores. By varying laser frequencies (4, 7, and 10 kHz) at a constant 50% duty cycle (DC), they found that higher frequencies resulted in smaller melt pools and shallower depression zones causing pores to form closer to the substrate-powder interface as seen in Fig 23. Conversely, when varying the duty cycle while keeping the frequency constant, a higher duty cycle produced larger melt pools and deeper depression zones causing pores to form deeper within the material. Figure 23: Pore formation in Al6061 under varying laser frequencies (4,7 &10kHz) at a constant duty cycle (50%DC) for a laser power of 470 W and a scan speed of 400 mm/s [96]. The LPBF process can be considered a scaled down version of laser welding to produce intricate parts with high precision [70]. The principles that govern laser welding such as energy absorption, melt pool dynamics and material selection are directly applicable to LPBF. One notable similarity is how both processes manage the absorption and distribution of laser energy [141]. When laser energy is focused on the material, photons are absorbed and converted into heat which then dissipates through conduction or convection. This heat must be 32 sufficient to melt the material without causing excessive vaporisation or other defects. The ability to control the laser power and other process parameters is crucial in both processes for achieving the desired outcome, whether it's a strong weld or a finely detailed additive part. In both processes, melt pool flow is driven by factors such as Marangoni convection and recoil pressure [142]. For instance, turbulent flow within the melt pool during keyhole mode can result in porosity formation [70]. Similar to laser welding, LPBF parts are prone to hot cracking caused by dendritic growth during solidification and high residual stresses [143]. These challenges restrict the use of LPBF to primarily weldable alloys. Naeem [144] pointed out the effectiveness of using pulsed laser welding to join dissimilar materials, specifically Haynes 230 (a Ni-Cr-W-Mo alloy) and Waspaloy (a Ni-Cr-Co superalloy). Both alloys are known to be prone to cracking when welded individually, making it crucial to ensure weld quality by preventing cracking and porosity in the fusion zone. Initially, a dissimilar weld joint between the two nickel-based alloys was made using continuous wave (CW) laser with nitrogen and argon shielding gases. As illustrated in Figure 24, both approaches resulted in either interface cracking or porosity. However, the use of pulsed laser welding on the same materials produced a weld joint free of porosity and interface cracking. Figure 24: Weld microstructure using continuous wave laser with a) Nitrogen shielding gas showing interface cracking b) Argon shielding gas showing entrapped porosity c) Weld microstructure using pulsed laser welding [144]. Laser pulsing has been a proven technique in welding for reducing porosity and improving the melt pool flow [133,145]. This technique stabilises the keyhole, decreases bubble formation by stirring the molten pool and facilitates the escape of trapped gases, thus improving weld microstructure and overall weld quality [146]. Given the similarities between welding and SLM processes, it is plausible that the benefits of laser pulsing observed in welding could also apply to SLM. Reducing porosity in SLM is crucial for enhancing the mechanical properties and reliability of the manufactured parts. In this work, I am exploring whether beam pulsing in SLM can stabilise the keyhole and minimise porosity similar to its effects in welding. 33 Chapter 3 – Scope and Objectives The goal of this work is to provide preliminary insights for our industrial partner (CANMORA TECH Inc.) on the feasibility of beam pulsing in laser powder bed fusion process as a mechanism for controlling melt pool geometry and particularly defects when melting under conditions that lead to keyholing. As underscored in the literature review [70,115], operating in keyhole melting mode enhances laser energy absorption, improves control over melting dynamics and offers the potential for universally applicable process parameters across diverse manufacturing platforms. The hypothesis posits that by beam pulsing one could manipulate the heat input and liquid flow. However, the impact of pulsing on porosity and melt pool shape under conditions analogous to keyhole melting is still not fully understood. The scope of this work has been limited to examining melt tracks of Ti-6Al-4V created by CANMORA TECH Inc. using Selective Laser Melting (SLM) in a relatively narrow range of beam parameters. The investigation centres on single melt tracks on both solid plates and powder with varying frequencies (10 Hz to 10 kHz) and duty cycles (25%, 50%, and 75%). Continuous tracks with four different power levels (125 W, 250 W, 375 W, and 500 W) were also created for comparison with the pulsed tracks. Furthermore, modelling using thermal only and thermal fluid (CFD) models were used to help better understand the experimental results, particularly the melt track geometry under keyholing conditions. 34 Chapter 4 – Methodology 4.1 Materials and Methods The Ti-6Al-4V samples investigated in this study were fabricated using a prototype additive manufacturing system developed by CANMORA TECH Inc. This system is equipped with integrated CTCAM software which allows users to modify system parameters according to production needs and maintain the line energy required for producing high-quality parts. The system employed a Yb-doped solid-state fibre laser with a wavelength of 1080 nm, capable of a maximum power output of 500 W and a scan speed of up to 200 mm/s. The laser optics and surrounding region to the print bed are surrounded by an enclosure within which argon gas is purged to control the atmosphere in the vicinity of the melt pool. For the samples discussed in this thesis, the melt tracks were performed by CANMORA TECH Inc. and the samples were then provided to UBC. A build plate with dimensions of 100 x 50 x 10 mm was fabricated from a forged Ti-6Al-4V alloy plate sourced from TIMET (Dallas, USA) [147]. For melt tracks produced in powder, gas-atomized Ti-6Al-4V powder purchased from AP&C were used [148]. The material data sheets provided by the supplier indicate a powder size distribution of D10 = 53 µm, D50 = 70 µm and D90 = 101 µm. Melt tracks were produced in these experiments both on the solid build plate as well as on the same plate after a 0.2 mm thick layer of Ti-6Al-4V powder was manually spread over the surface using a rake. As shown in Fig. 25, the beam power was ramped up at a constant rate from 100W until it reached full power within 24ms. A steady-state condition was reached after the transient portion where the behaviour of the system became independent of time i.e., any time-dependent effects were negligible on timescales longer than the duration of a single pulse. Similarly, the power was ramped down at the end of the track in the same manner. These “run in” and “run out” conditions were used to limit the strong, uncontrolled transients that occur in the melting when the beam is suddenly turned on or off. For the purposes of this study, however, the focus is on the “steady state” portion of the melt tracks. Basic terminologies such as duty cycle and frequency were previously discussed in section 2.3.4. For pulsed experiments, a signal generator capable of generating periodic signals at frequencies into the MHz range was connected to the power supply for the laser. In this way, high frequency square pulses could be generated and used to control the power of the laser. It must be noted that the power in the generated laser was not directly measured and so for the purposes of this study all reported pulsing and power conditions are those requested of the system. It is important to note that the high power and pulsed experiments carried out here were performed at 500W, the maximum output power of the laser. Discussions with CANMORA TECH Inc. indicate that operating at this upper limit of the power will lead to the largest 35 possible variation of upto 10% in the actual delivered laser power. Several minutes were allowed to pass between each melt track’s production allowing time for any buildup of temperature in the build plate to be dissipated. In this way it is expected that each melt track was produced with the same (ambient) background temperature. Figure 25: Section of the base plate showing melt tracks at different frequencies for 50% duty cycle, illustrating the steady-state portion and transient portion of the melt track. The time and distance travelled by the beam during the on/off transitions for each frequency. 4.2 Microstructural Characterization The base plate was sectioned into different samples and each sample was then cut cross-sectionally to analyse the melt pool geometry and defects using wire cutting Electrical Discharge Machining (wire-EDM). One part of each sample was then cold mounted in epoxy for ease of polishing. The samples underwent a multi-step polishing process to prepare the surfaces for metallographic examination. Initially, rough polishing was performed using silicon carbide papers with grit sizes of 600, 800 and 1200. This was followed by fine polishing 36 with diamond suspensions of 6μm and 1μm and finally with 0.5μm colloidal silica to achieve a smooth mirror like finish. After polishing, the samples were etched with Kroll's reagent which is composed of 2% Hydrofluoric acid (HF), 5% Nitric acid (HNO3) and 93% water (H2O). The melt pool morphology and microstructure of all samples were characterized using a digital optical microscope ‘Keyence VHX -7000’ which allows for detailed imaging and analysis. The surface roughness measurements of each sample were taken with Keyence prior to the cutting of the base plate. Figure 26:Keyence VHX-7000 4.3 Porosity measurements Micro-computed tomography (μCT) is a non-destructive 3D imaging technique that reconstructs the 3D structure of an object by taking a series of 2D projection radiographs from different angles [149]. MicroCT are commonly used for the detailed visualization and quantification of pores within printed components [150]. The samples for MicroCT analysis were cut from the baseplate, spanning from the middle of the steady-state portion of the melt track to its end (2-4mm in length) as shown in Fig. 25. Data from the ramp-out region located within roughly one millimetre from the end of the melt track were excluded from the analysis to avoid the effects of transient conditions. In this study, a Zeiss Xradia Versa 520 X-ray microscope operating at 160kV with a 3.72μm voxel scan was used to investigate the internal porosity distribution in SLM-processed samples as seen in Fig 27. During the sample rotation, 1601 projections were collected with an exposure time of 1.8s with a total scan time of 1.5 hours. The projections were then converted into a TIFF stack image and processed using ImageJ and Dragonfly software to analyse the porosity within the samples. Porosity refers to small air pockets or cavities within a material often resulting from trapped gases during the manufacturing process. Understanding and studying porosity is crucial because it significantly affects the mechanical properties of the material. 37 Figure 27:Zeiss Xradia Versa 520 system. 4.4 Computational Modelling This section deals with the computational models that were used to replicate experimental conditions under various pulsing parameters. This approach aids in understanding the mechanism behind porosity formation and predicting melt pool geometry across different process parameters. In certain scenarios, extensive physical experiments can be avoided by utilising computational models making it more cost-effective and time efficient. The FAST model which is an abbreviation for Fast-running Semi-Analytical Self-consistent Thermal model [151] is a simplified heat transfer model that solves the heat equation (Fourier’s Second Law of Heat Conduction) in a semi-analytical format meaning that the time consuming step of numerical solution of the heat equation normally needed in techniques like the FEM can be avoided. The FAST model is able to efficiently simulate temperature distributions even in scenarios involving moving heat sources and complex geometries. The concept of this semi-analytical model was first proposed by [66] and has been further developed in subsequent studies [151,152]. The original semi-analytical approach is based on simplifying the problem to a linear form by neglecting non-linear effects such as the temperature dependence of material properties, radiative or convective heat loss and a semi-infinite domain (no complex boundary conditions). In the FAST formulation, these non-linear effects (particularly temperature dependent material properties, radiation heat loss and adiabatic boundary conditions) have been approximately reintroduced into the model allowing results that much more closely match those from other techniques. Through these advancements, the FAST model can replicate predictions from FEM 38 calculations at a fraction of the computational cost as will be illustrated later in this thesis. Unlike traditional semi-analytical models which assume constant material properties, the FAST model incorporates temperature-dependent properties by averaging the material properties based on the local temperature. In this work, the focus is on predicting the melt pool dimensions and so the model uses only those material properties closest to the liquidus temperature. As the model is purely a heat transfer model it does not include effects arising from fluid flow and so treats regions above the liquidus temperature as “solid-like” but with enhanced thermal diffusivity. Table 2 and Fig 28 provides the material properties used for the modelling in this case. Table 2: Material properties used in the FAST model. Values taken from those used in reference [151]. Parameter Units Value Density of metal kg/m3 4200 Solidus temperature K 1843 Liquidus temperature K 1923 Figure 28: The temperature-dependent material properties (specific heat and thermal conductivity) of Ti–6Al–4V used in the FAST model [151]. 39 The domain for the FAST model is semi-infinite with the top surface being subjected to radiative heat transfer conditions. Heat was input into the top surface via a gaussian shaped surface heat flux (for more details, see [151] ). Calculations are performed only at specified locations. Two options were used for the calculations presented here. In the first option, a fixed set of calculation points was used forming a roughly hemispherical shell beneath the beam location. The number of points was automatically adjusted within the FAST code to ensure that the melt pool volume defined by the liquidus temperature was always calculated. In this case, the spacing used between points was 10 x 10 x 10 µm. In second type of calculation, a fixed grid of calculations points was chosen that covered the full path travelled by the simulated beam. These points were selected to have a similar spacing (10 × 10 × 10 µm) as those in the "melt pool" model calculations. The timestep for data output was varied depending on the pulsing frequency ensuring that data was output more than 10 times for each cycle. Initially, the model predicts temperature distribution assuming heat transfer occurs solely through conduction within a semi-infinite domain. Radiation is introduced as a correction by scaling the input power. The model iteratively adjusts temperature predictions based on Stefan-Boltzmann’s law which governs radiative heat transfer. The degree of heat loss by radiation is then controlled both by the local temperature and the emissivity which is treated here as an adjustable parameter. This will be discussed in the section outlining the model results. The FAST model also assumes a gaussian shaped heat input from the moving laser to the surface of the material as a heat flux boundary condition. The “footprint” of the laser beam on the sample used experimentally was not known to high accuracy in this study, its width (standard deviation) being between 0.1 and 0.2 mm. The standard deviation (σ) in this case represents the width of the beam's intensity distribution at the point where the intensity falls to 1/e² of the maximum intensity. For the purposes of modelling, a standard deviation of 0.145 mm was assumed as a baseline. It will be shown, however, that this choice has little effect on the results under the conditions considered. Finally, it is known that the laser’s energy is absorbed with only partial efficiency. In the modelling, this has been accounted for through an efficiency (η) parameter that multiplies the laser power and is treated as a fitting parameter. A higher-fidelity CFD model was employed which accounts for fluid flow characteristics such as Marangoni flow and surface tension to better understand the mechanism behind porosity formation, particularly in keyholing conditions. This model is based on the ‘LaserBeamFoam’ simulation package which is a specialised computational solver designed for multiphase thermal fluid dynamics. The foundational work for this package is referenced in [153], with updates to the software detailed in [154]. An example of its application to study the LPBF process is provided in [155]. The material properties used for the CFD calculations are also taken from [153] and are reported in Table 3. The CFD model is governed by three key conservation equations: the Navier-Stokes equation which describes the conservation of momentum in a fluid by accounting for forces 40 such as pressure and viscosity. The volume-of-fluid (VoF) equation deals with the conservation of mass by tracking the interaction and movement of different phases (e.g., liquid and gas) within the system. The energy transport equation deals with the conservation of energy, tracking the flow of heat and its impact on the temperature distribution in the material. The model incorporates a ray-tracing algorithm that tracks the interaction of laser rays with the material surface to determine the amount of energy absorbed. As the laser enters the domain, it gets split into multiple rays at the boundary. The number of rays is determined by the mesh resolution and the number of cells that fall within the laser's radius at the boundary. Each ray is assigned a direction and its path is tracked as it travels through the domain. When a ray hits a metallic cell, the software calculates the amount of energy absorbed and reflected using Fresnel’s equations. The reflected ray is then given a new direction and this process continues until the ray either loses all its energy or exits the domain undergoing multiple reflections. Table 3: Material properties used in CFD model. Values taken from those used in reference [153]. Parameter Units Value Parameter Units Value Density of metal kg/m3 4420 Solidus temperature K 1878 Thermal conductivity of metal W/m.K 10 + 0.015T Liquidus temperature K 1928.15 Specific heat J/kg.K 520 + 0.075T Evaporation temperature K 3558 Latent heat of fusion J/kg 2.96 x 105 Surface tension of liquid J/m2 1.7 Kinematic viscosity of liquid m2/K 1.35 x 10-6 Surface tension dependence on temperature (dσ/dT) J/m2.K -3.6 x 10-4 Thermal conductivity of gas W/m.K 0.04 Heat of vaporization J/kg 8.88 x 106 41 Figure 29: The domain size used in the CFD model. The simulation domain was 1 x 1.5 x 1.5 mm in size with a grid size of 12.5 x 12.5 x 12.5 µm as seen in Fig 29. Zero flux boundary conditions were used on all sides of the simulation box. Several assumptions are made in this model. It is based on a finite volume CFD algorithm employing a volume-of-fluid (VoF) approach to capture phase properties. All phases (solid, liquid, and gas) are assumed to be incompressible, meaning their density does not change under pressure. As a result, the velocity field must be divergence-free. It ensures that the fluid's volume stays constant with no accumulation or depletion at any point. This means that fluid moves without changing its density maintaining mass conservation. The velocity and pressure fields are calculated from the Navier-Stokes equations under this assumption. The buoyancy effects are modelled using the Boussinesq approximation which assumes that density variations are negligible. In the final chapter, the objective is to compare the results from both the FAST model and the CFD model with experimental data to assess their accuracy in predicting melt pool geometry under selected process parameters. 42 Chapter 5 – Experimental Results 5.1 Measurements of Melt Track Geometry from Surface Measurements As noted above, this study aims to quantify the impact of pulsed and non-pulsed laser beams on the melting behaviour of Ti-6Al-4V. This chapter will describe the effects of different pulsing conditions on surface roughness and melt pool geometry in single melt tracks. The experimental observations using optical metallography (Keyence) and surface height measurements to analyse the melt tracks will also be discussed. Fig 30 illustrates the fifty melt tracks produced for this study. The 25 melt tracks (labelled 1-25) on the left side of Fig 30 were made directly on the Ti-6Al-4V base plate. The 25 melt tracks on the right side of Fig 30 (labelled 1-25) were made after the base plate had been covered with a single layer, approximately 0.2 mm deep of the Ti-6Al-4V powder described in Section 4.1. All the process parameters used for making these tracks are shown in Table 4. The melt tracks shown in Fig 30 were produced by varying the laser pulse frequencies between 10Hz to 10kHz with duty cycles of 25%, 50% and 75%. In each case the laser power and velocity were kept fixed at 500W and 50 mm/s respectfully (as described in Section 4.1). In addition, four melt tracks were made without pulsing the laser. These continuously melted tracks (denoted “CW” in Fig 30) were made at four distinct power levels: 125W, 250W, 375W and 500W. The first three of these reflect the average power input at 25, 50 and 75% duty cycle using 500W. To distinguish between different melt tracks, the numbering scheme in Fig 30 is used prefaced by “P” for melt tracks produced in powder and “S” for melt tracks made on the solid base plate. Figure 30: Ti-6Al-4V Base plate with the solid melt tracks (labelled 1-25) on the left side and powder melt tracks (labelled 1-25) on the right side. The corresponding pulse frequencies and duty cycles are mentioned on the left and last row respectively. 43 5.1.1 Melt Track Observations Fig 31 shows the continuous melt tracks produced in the solid base plate for four distinct power levels. One can see a noticeable variation in the melt track width at both the beginning and the end of the track. This variation is primarily attributed to transients occurring at the beginning and end of the melt track but also due to the imposed power ramping (described in Section 4.1) used to ‘run in’ and ‘run out’ the melt track. This effect is also reported in [156]. For melt tracks made in the solid base plate, at the start of the scan, as the laser power ramps up, the melt track width gradually increases until it reaches a steady state. Similarly, at the end of the scan, as the power ramps down and the laser is turned off, the melt track forms a depression as the material retracts, akin to beam sculpting [157]. The focus here is on characterising in the steady-state portion. Table 4: The various combinations of process parameters used in the samples. Sl No Frequency (Hz) Duty Cycle (%) Speed (mm/s) Peak Power (W) 1 10000 25 50 500 2 10000 50 50 500 3 10000 75 50 500 4 5000 25 50 500 5 5000 50 50 500 6 5000 75 50 500 7 1000 25 50 500 8 1000 50 50 500 9 1000 75 50 500 10 500 25 50 500 11 500 50 50 500 12 500 75 50 500 13 100 25 50 500 14 100 50 50 500 15 100 75 50 500 16 50 25 50 500 17 50 50 50 500 18 50 75 50 500 19 10 25 50 500 20 10 50 50 500 21 10 75 50 500 22 CW - 50 500 23 CW - 50 375 24 CW - 50 250 25 CW - 50 125 44 Figure 31:Continuous solid melt tracks for various powers a) 500W, b) 375W, c) 250W & d) 125W as viewed by the Keyence. Figure 32: Steady state continuous solid melt tracks observed at higher magnification compared to Fig 29 for four power levels ranging from 125W to 500W. Fig 32 depicts the steady state solid melt tracks for four distinct power levels: 125W, 250W, 375W & 500W. It is evident that as the power increases, the melt track becomes larger and wider due to more heat input into the material. Figure 33:Continuous powder melt tracks for various powers a) 500W, b) 375W, c) 250W & d) 125W as viewed by the Keyence. 45 Melt tracks created in powder under identical ramp-in and ramp-out conditions exhibit differences in morphology as shown in Fig. 33. While the end of the melt track shows a similar depression [152], the start of the melt track exhibits a bulged portion. This bulged portion at the beginning is sometimes referred to as ‘balling’ in AM literature [82,153]. Figure 34:Steady state continuous powder melt tracks at higher magnification compared to Fig 31 for four power levels ranging from 125W to 500W. When comparing Fig 32 and 34, the powder melt tracks appear narrower than their solid counterparts. Another intriguing observation in powder melt tracks is that the amount of unmelted powder decreases as the power increases. Andani et al. [127] similarly observed that reducing the power level led to an increase in unmelted particles. It was proposed in their work that the heat input might not spread sufficiently to melt all the powder particles, particularly those farther from the centre of the laser spot. As a result, more unmelted particles remain on the surface. Figures 35 demonstrate the impact of pulsing frequency and duty cycle on the stability of solid melt tracks. At low pulsing frequencies and duty cycles, like 10 Hz and 50 Hz, discontinuous tracks were observed. Two magnifications are provided for these cases: the upper image shows a lower magnification, with the red box highlighting the area that is further magnified and displayed below. To illustrate, at 10Hz, each pulse lasts 100 milliseconds and with a 50% duty cycle, the laser is active for only half of this period i.e., 50 milliseconds. During this time, the laser beam travels 2.5mm creating a segment of the melt track. However, for the next 50ms, the laser is off resulting in a 2.5mm gap where no melting occurs assuming that the melt track formation immediately ends when the beam is turned off. When the laser turns on again, it forms another separate segment leading to a discontinuous melt track. At higher frequencies, such as 10 kHz and 5 kHz, the laser pulses occur rapidly, leading to melt tracks that closely resemble those produced in continuous mode across all duty cycles. In these cases, as the average power increases (which corresponds to a higher duty cycle in the pulsed mode), the melt track width also increases similar to the continuous case. The shorter interval between pulses means that the laser turns on again before the melt pool has had time to solidify completely, keeping it in a liquid state. The rapid succession of pulses and subsequent continuous energy input allows the formation of a continuous and stable melt track. 46 Figure 35: Steady-state solid melt tracks are shown for various pulsing frequencies (ranging from 10 kHz to 10 Hz) across three different duty cycles (25%, 50%, and 75%). Smaller magnification images are provided at lower frequencies (50 Hz and 10 Hz) to indicate the regions from which the higher magnification images (red box) were captured. 47 In Figure 35, when analysing the melt tracks across the row where the frequency remains constant but the duty cycle varies, you'll notice that the melt track width increases as the duty cycle increases. This occurs because the laser beam stays active for a longer duration during each pulse, delivering more heat and therefore producing a wider melt track. However, when you look down the column, where the duty cycle is constant while the pulsing frequency is varied, the melt track edges start to appear more irregular, taking on a wavy shape and the track width also increases. This irregularity can be attributed to the Gaussian profile of the laser beam which has a circular impression when it falls on the substrate. At frequencies such as 100Hz, the overlap between consecutive pulses is insufficient leading to a wavy pattern at the edges of the melt track. Although this issue has not been specifically reported in laser pulsing studies, similar effects have been observed in research on laser beam shaping [158]. There appear to be some transitional conditions such as 1kHz and 500Hz at 25% and 50% duty cycles and 100Hz at 50% and 75% duty cycles, where the melt tracks are neither fully separated nor like the continuous ones. Figure 36 shows the impact of pulsing frequency and duty cycle on the stability of powder melt tracks. The powder melt tracks exhibit narrower widths and more irregular surfaces compared to the solid melt tracks. The intermittent energy delivery has led to irregularities in some of these cases as seen in Fig 37. For instance, in Fig 37 (b), at the start of the melt track, a bulged portion is observed followed by a prominent tail or depression at the end, much like in the solid tracks which is also reported in [159]. At the beginning of the melt track, the laser beam first interacts with the powder bed where the material is cold. It requires energy to raise the temperature of the surrounding. Since a melt pool is not established, heat transfer is inefficient as it must pass through the powder which has low thermal conductivity. This causes heat to concentrate near the surface. Once a melt pool forms and connects to the baseplate, thermal conduction improves enabling more efficient heat transfer. Also in pulsing, there might be insufficient energy during certain phases to fully melt the powder particles. These particles can then be either partially melted or remain completely unmelted, especially if they are on the periphery of the melt pool where the energy is lower. These unmelted particles can adhere to the single beads formed by the laser leading to a rougher surface texture. As the process continues and more heat accumulates, a stable melt pool forms leading to more consistent width along the melt track. Returning to Fig 36 (at 50Hz and 10Hz) and 35(c), discontinuous tracks are observed. These arise from using very low pulse frequency and duty cycle, where the laser beam gets cut off, leading to discrete melt tracks. Through experimentation, it is observed that a minimum frequency of 100 Hz is required to produce continuous melt tracks for our selected process parameters. Frequencies below this threshold will lead to the formation of discontinuous tracks at the constant scan speed of 50mm/s which are undesirable for producing high quality parts. 48 Figure 36: Steady-state powder melt tracks are shown for various pulsing frequencies (ranging from 10 kHz to 10 Hz) across three different duty cycles (25%, 50%, and 75%). Smaller magnification images are provided at lower frequencies (50 Hz and 10 Hz) to indicate the regions from which the higher magnification images (red box) were captured. 49 There are transitional conditions similar to those observed in solid tracks, where the melt tracks are neither fully separated nor resemble the continuous ones. In this study, the focus will be on analysing the steady-state portions of these melt tracks for further examination. Figure 37: Illustrating various irregularities in pulsed powder melt tracks: a) 5kHz-50% DC showing denudation and necking formation, b) 5kHz-75% DC showing bulging or ‘balling’ and c) 50Hz-50% DC showing discontinuous melt tracks. 5.1.2 Measurements of the Melt Track Width The rationale behind measuring the melt track width stems from the need to quantify the qualitative observations made in section 5.1.1. While initial observations provided a broad understanding of the melt track characteristics, these width measurements will allow us to compare and understand the relationship between laser parameters and the resulting melt pool geometry. This is particularly important as it enables us to validate and refine the models that will be discussed in the next chapter. Fig 38 illustrates the method for measuring the experimental melt track width. About 50 parallel lines were drawn perpendicularly at equal intervals from the top to the bottom of the melt track with measurements done using Origin Pro software. Figure 38: Methodology for measuring experimental melt track width. 50 Figure 39: Variation in melt pool widths for continuous (CW) solid melt tracks (round dots) and powder (hollow rectangles) melt tracks as a function of power. Powder melt tracks are prefaced by ‘P’ while ‘S’ for melt tracks made on the solid base plate. Figure 39 illustrates the variation in melt pool widths for continuous solid melt tracks (round dots) and powder (hollow rectangles) under various power conditions. As observed in our initial qualitative analysis, the melt pool width in both solid and powder tracks increases with power. However, in powder melt tracks, the presence of unmelted powders introduces variability and noise to the data. To achieve a more reliable comparison, the mean and standard deviation of the melt pool widths for the melt tracks were calculated and plotted for the corresponding power, which helps account for the variability caused by these unmelted powders. The grey dotted lines represent the standard deviation of the melt pool widths for the powder melt tracks at each power level while the standard deviation for the solid melt tracks is shown in the same colour as the reference track for comparison. Powder melt tracks typically exhibit smaller melt pool widths compared to solid melt tracks at same process conditions, a trend also reported in studies by [160]. Figure 40 (a, b & c) illustrates the variation in melt pool widths for solid melt tracks at different frequencies and duty cycles (25%, 50% and 75%). At 25% and 50% duty cycle, it is observed that intermediate frequencies like 100Hz and 500Hz tend to produce wider melt tracks compared to both continuous (CW) and 51 high frequency pulsed tracks for the same average power. Figure 41 (a & b) provides a closer look at the 50% duty cycle, offering a detailed breakdown of what was shown in Figure 40b. In this figure, high frequency pulsed and continuous tracks display minimal differences in melt track widths. However, there is a substantial difference when it comes to intermediate frequencies. As the duty cycle increases to 75% (Fig 40c), the differences in melt pool widths become less noticeable. This is because at higher duty cycles, the laser remains active for a longer portion of each cycle delivering more continuous energy input and maintains a more uniform thermal gradient resulting in more consistent melt track widths across different frequencies. Figure 40: Variation in melt pool widths of steady-state solid melt tracks across different pulse frequencies at (a) 25% duty cycle, (b) 50% duty cycle and (c) 75% duty cycle. 52 Figure 41: Magnified sections of Fig 40 (b) highlighting the differences in melt pool widths between (a) intermediate frequencies and continuous 250W power and (b) high frequencies and continuous 250W power. Figure 42 (a, b, c) illustrates the variation in melt pool widths for powder melt tracks at different frequencies and duty cycles (25%, 50%, and 75%). Similar to the observations in Fig 40, the presence of unmelted powder introduces variability and noise into the data. To account for this, the mean and standard deviation were calculated and included in the legend for each pulsing condition. Melt pool widths in powder tracks are smaller than those in solid tracks under pulsing conditions. However, due to the inherent variability, no clear trend emerges across all pulsing conditions. When comparing the mean from each pulsing frequency, it is observed that at 25% and 50% duty cycles, intermediate frequencies such as 100Hz and 500Hz tend to produce wider melt tracks compared to both continuous and high-frequency pulsed tracks. Despite this, there isn't a significant difference in melt pool widths across the duty cycles, unlike the noticeable variations seen in solid melt tracks under similar conditions. 53 Figure 42: Variation in melt pool widths of steady-state powder melt tracks across different pulse frequencies at (a) 25% duty cycle, (b) 50% duty cycle and (c) 75% duty cycle. The number in the brackets represents the mean and standard deviation for each pulsing condition in the powder melt tracks. 5.1.3 Surface roughness Higher surface roughness increases the likelihood of crack initiation leading to lower fatigue life [161]. Studies also indicate that higher surface roughness correlates to a larger tendency for internal defects in a part [162]. Fousova et al.[163] pointed out that surface irregularities are often the starting point for crack formation and it affects mechanical properties such as tensile strength and increases susceptibility to corrosion. Moreover, if the surface roughness is too high, it can hinder the proper spreading of the next layer of powder. With pulsing and the potential for different liquid flow patterns, it was desired to see whether the surfaces of melt tracks in pulsed conditions varied compared to continuous melt tracks. 54 Figure 43:Variation of surface roughness of continuous solid melt tracks for various powers a) 125W b) 250W c) 375W & d) 500W. The arithmetic average height (Ra) is a widely used metric for evaluating surface quality. It provides a measure of the overall roughness by calculating the arithmetic average of deviations from the mean line over a specified sampling length. [163] A larger Ra value indicates greater surface roughness with more pronounced deviations, while a smaller Ra suggests a smoother surface. Ra reflects the general surface profile and is less sensitive to individual peaks or valleys. The average roughness measurements of each sample were obtained using the Keyence microscope, with four parallel lines measured to assess the surface profile variation. Fig 43 displays the surface profile for continuous solid melt tracks across various power levels. The observed variation in Ra values, ranging from 1.4 to 2.63µm, can be attributed to the effects of increasing power on melt pool dynamics. As power increases, the higher energy input intensifies the fluid flow within the melt pool, resulting in pronounced surface undulations or ridges that form opposite to the beam direction. 55 Figure 44: Variation of surface roughness of continuous powder melt tracks for various powers a) 125W b) 250W c) 375W & d) 500W. Figure 44 presents the surface profiles of continuous powder melt tracks with average Ra values ranging from 34.43µm to 43.01µm. Unlike the trends observed in solid melt tracks, no consistent pattern was evident here. This lack of a trend can be attributed to the presence of unmelted powders and defects on the surface which significantly degrade surface quality and lead to higher Ra values. Figure 45 illustrates the variation in the surface profile of solid melt tracks at different pulsing frequencies at a constant duty cycle of 50%. The variation in Ra values across different pulsing frequencies can be attributed to how the melt pool forms and solidifies. During the pulsing process, the laser emits energy in bursts which leads to localized melting of the material. However, as the laser turns off between pulses the molten material rapidly solidifies. This cyclical pattern of heating and cooling introduces uneven cooling rates across the melt track causing the material to cool and contract irregularly. These irregular contractions give rise to the formation of ridges or undulations on the surface. 56 Figure 45: Variation of surface roughness of solid melt tracks for different pulsing frequencies at 50% duty cycle a) 10kHz-50% duty cycle, b) 5kHz-50% duty cycle, c) 1kHz-50% duty cycle, d) 500Hz-50% duty cycle & e) 100Hz-50% duty cycle 57 The more pronounced the surface irregularities, the rougher the surface which is directly influenced by the frequency and the nature of energy delivery during the pulsing process. As observed in Fig. 45(e), surface undulations are less pronounced at lower frequencies, but they become more prominent as the frequency increases. It also reveals that the dips or troughs in the surface profile correspond to the periods when the laser beam is turned off. Figure 46 shows the surface roughness of solid melt tracks as the duty cycle varies at a constant frequency of 500Hz. The data reveals that the Ra value increases to 6.39µm when the duty cycle is set to 50% but decreases again to 2.98µm at a 75% duty cycle. The reason for this fluctuation is not clear. Figure 46:Variation of surface roughness of solid melt tracks for (a) 25% duty cycle, (b) 50% duty cycle and (c) 75% duty cycle at a constant frequency of 500Hz. Figure 47 examines the surface profile of powder melt tracks at different pulsing frequencies at a constant duty cycle of 50%. The average Ra values are notably high, indicating poor surface quality. This suggests that pulsing has a detrimental effect on the surface quality of powder melt tracks compared to solid tracks. Figure 48 shows the surface roughness of powder melt tracks as the duty cycle varies at constant frequencies of 500Hz and 100Hz. The data suggests that 25% duty cycle yields the least rough surface profile among the three duty cycles. However, even at 25%, the surface quality remains inferior to that of solid melt tracks highlighting the difficulties in attaining smooth surfaces in pulsed powder melt tracks. 58 Figure 47: Variation of surface roughness of powder melt tracks for different pulsing frequencies at 50% duty cycle a) 10kHz-50% duty cycle, b) 5kHz-50% duty cycle, c) 1kHz-50% duty cycle, d) 500Hz-50% duty cycle & e) 100Hz-50% duty cycle. 59 Figure 48:Variation of surface roughness of powder melt tracks for different duty cycles at a constant frequency of 500Hz and 100Hz. a) 500Hz-25% DC, b) 500Hz-50% DC, c) 500Hz-75% DC, d) 100Hz-25% DC, e) 100Hz-50% DC & f) 100Hz-75% DC. 60 5.1.4 Melt track morphology – Cross section Table 5: Compilation table of melt pool geometry and aspect ratio for continuous and various pulsed conditions for both solid and powder melt tracks. To analyse the variation in melt pool depth and width, all the melt tracks were cross -sectioned close to their midpoint. Each section was then mounted and the melt pool width and depth were directly measured from similar cross-sectional microscopic images as illustrated in Fig 9. Table 5 shows the melt pool geometry and aspect ratio for various pulsed and continuous melt tracks. The aspect ratio is calculated as the ratio of the melt pool depth by the melt pool width. In Figure 49, the melt pool cross section is plotted against varying frequencies and duty cycles. The figure shows keyholing conditions as outlined in the literature review for all cases except those with low frequencies and low duty cycles. For continuous melt tracks, the melt pool depth (MPD) decreases with decreasing power which is expected due to the reduced heat input available for melting. The 125W melt track appears to be near the conduction or transition regime with no observed porosity while the other tracks are in the keyhole regime where porosity is evident. However, caution is necessary when interpreting these results as the cross-section was not taken through the exact centre of the melt track, meaning that these cross-sections may not represent the true view of the melt pool’s shape. The red box highlights that the cross-sections may not reflect steady-state conditions making them unsuitable for representative comparison of melt pool geometry. 61 Figure 49:Solid melt pool cross-section for various pulsing frequencies at different duty cycles. Continuous solid melt track cross-section is given at top for reference. Melting mode has a crucial impact on the size and geometry of melt pools in LPBF. If we look at the melt pool morphology for CW 125, the top of the melt pool is almost like a V shape. However, in some studies, the melt pools have been observed to exhibit a bimodal shape with a bowl on top and a spike at the bottom [68,70]. This is attributed to fluid flow effects such as the Marangoni flow which causes the melt pool to flow outward, resulting in an increased melt pool width. The recoil pressure pushes the molten material downwards, leading to the formation of a spike at the bottom of the melt pool. Similar observations have been reported in 62 [165,166]. As we look down the column where the duty cycle decreases, we see a corresponding decrease in melt pool depth. This trend aligns with expectations: a lower duty cycle means the laser is on for less time, which reduces the heat input and results in a shallower melt pool. However, across the row, where the duty cycle remains constant but frequency varies, the MPD exhibits a "hill-like" pattern. For instance, at a 50% duty cycle, the MPD is 0.94mm at 10kHz, decreases to 0.83mm at 5kHz, then increases again to 0.93mm at 1kHz and further rises to 1.17mm at 500Hz. The reason for such a pattern remains unclear as it has not been reported in existing literature. Figure 50: Powder melt pool cross-section for various pulsing frequencies at different duty cycles. Continuous powder melt track cross-section is given at top for reference. 63 Figure 50 illustrates the melt pool cross-sections for various pulsing frequencies at different duty cycles in powder melt tracks, revealing trends similar to those observed in solid tracks. However, a notable difference is the larger size and amount of porosity in powder tracks compared to solid ones. When comparing the two, solid tracks exhibit greater melt pool width and depth. This can be attributed to several factors discussed earlier including the efficiency of laser absorption in powders. As the laser heats the powder material, localized melting and vaporisation create a vapour plume, which generates a low-pressure region around it due to the Bernoulli effect [166]. This low-pressure zone can draw surrounding powder into the melt pool. While this brings more material into the melt pool, it also means that the available energy is distributed over a larger amount of powder, resulting in less effective melting. This influx of material can further trap gases and contribute to the formation of larger pores within the melt pool. 5.2 Micro-computed Tomography Observations Rather than performing MicroCT scans on all the samples, a subset of samples was selected based on specific observations. When revisiting the melt pool widths for solid tracks in Fig. 41, the intermediate frequencies stood out as particularly interesting compared to others. Unlike higher frequencies, which closely resembled continuous tracks and frequencies below 100Hz, which resulted in discontinuous tracks. The intermediate frequencies produced something that is neither separated nor like the continuous tracks. Based on this, the MicroCT analysis was focused on one of the intermediate pulsed conditions: 500Hz at 50% duty cycle and compared it with a continuous melt track at 250W (chosen for its equivalent average power) on both solid and powder samples. This approach allows us to assess how porosity differs between pulsed and continuous conditions. I focused on four major characteristics of porosity in continuous and pulsed samples: the number density or volume fraction of pores, pore shape, pore size and the spatial location of the pores. The process for calculating the volume fraction of pores in continuous solid and powder samples is detailed below. First, the cross-sectional images captured using the Keyence microscope (Fig. 50 & 51) were imported into ImageJ software. The area of the melt pool cross-section was calculated and this area was then multiplied by the length of the melt track to determine the total melt pool volume. The total porosity was obtained through Micro CT post-analysis using Dragonfly software, where it was reported as a volume of voxels. The porosity was reported as a volume fraction rather than pores per unit length because the melt pool size varies between different pulsing conditions and continuous tracks making volume fraction a more accurate measure accounting for these variations. For the continuous 250W solid and powder samples, the total melt pool volumes were 1.08mm³ and 1.64mm³ respectively, with corresponding porosity volume fractions of 0.009 and 0.002. The impact of power on porosity volume fraction was also studied. For example, at 375W, the total melt pool volume in the solid 64 track was 3.71mm³ with a porosity volume fraction of 0.002. This indicates that porosity is higher when power is low and as it increases, the number of pores decreases. This aligns with our observations and is also reported in [167], where it was found that total porosity is strongly influenced by the power. Figure 51: Volume fraction estimation method in continuous a) 250W - solid and b) 250W - powder melt tracks. Figure 52 illustrates the spatial distribution of pores from both a top view (a) and a cross-sectional view (b) of the solid melt track providing insights into their depth and location. In the top view, depicted in Figure 52a, the porosity distribution within the continuous melt track does not reveal any distinct pattern. The x-axis represents the melt track's width on the base plate, the y-axis corresponds to its depth and the z-axis runs along its length. Figure 52b provides a side view of the melt track, where each pore is colour-coded to indicate its aspect ratio. These pores vary in size and shape with some being large and spherical while others are irregular and extend deep into the material. Du Plessis et al. [168] proposed that the smallest pore size that can be reliably detected should be at least three voxels in width. To minimise the influence of noise-induced pores, the minimum pore size was set at 27 voxels (3x3x3), consistent with other studies [170,171]. Figure 53a further illustrates how the pores are distributed along the length of the solid melt track and Figure 53b shows their distribution by depth. Most of the porosity occurs at a depth of 0.4-0.6 mm, indicating that these are likely keyhole pores formed due to the instability in the keyhole region during the laser melting process. The selected process parameters led to a keyhole mode rather than conduction or transition mode as discussed in the literature. The maximum depth at which porosity was observed is approximately 0.62 mm. This 65 depth aligns closely with the total melt pool depth for the continuous 250W solid melt track which was measured to be 0.65mm from metallography as shown in Table 5. Figure 52: Spatial profile of the porosity a) the top view b) side view. The yellow box in the top corner indicates the region of study for porosity in this 250W continuous solid melt track. The x-axis indicates the width of the melt track, y-axis - the depth of the melt track and the z-axis- the length of the melt track. The shape and size of the pores are quantified by the aspect ratio and Feret diameter. The Feret diameter is defined as the distance between two parallel tangents of a particle at any arbitrary angle [171]. It provides insight into the size of the pores within the sample. The aspect ratio is defined as the ratio of the maximum to the minimum Feret diameter. It indicates the shape of the pores and their degree of irregularity or elongation, with a higher aspect ratio signifying more irregular shapes. Figure 52b also illustrates the overall aspect ratio of the porosity within the melt track while Figure 54a shows that most pores are spherical with only a few irregular pores observed in the continuous solid melt track case. Figure 54b presents the spatial distribution of the pores and correlates it with the aspect ratio, offering a 2D view that clearly demonstrates how the pores are distributed across the melt track. The dotted line represents the melt pool depth as measured by the Keyence microscope with no porosity detected below this point. 66 Figure 53: Number of pores a) per unit length b) per unit depth in 250W continuous solid melt track. Figure 54: a) Number of pores per aspect ratio b) 2D representation of aspect ratio as a function of length and depth for the 250W continuous solid melt track. The dotted line represents the melt pool depth as measured by the Keyence microscope. 67 Fig 55 shows the cross-sectional view and the size of the pores as a function of maximum feret diameter along the melt track. There are a few pores with large feret diameter of about 0.20mm but most of the pores are relatively small as the Feret diameter is in the range of 0.02-0.10mm. When larger pores are situated closer to the surface, they act as crack initiators under cyclic loads increasing the likelihood of fatigue failure. This observation has been reported in [168]. Figure 55: a) Cross section of the 250W solid melt track showing variation of feret diameter b) Number of pores per feret diameter Figure 56 shows the spatial distribution of pores in the 250W continuous powder melt track both from a top view (a) and cross-section (b). Compared to Figure 52, which depicts the solid melt track under the same conditions, the powder melt track exhibited a greater amount of porosity. As in the solid case, there is no distinct pattern in the porosity distribution within the continuous melt track. Some porosity is also visible in the bead, which is the continuous metal layer formed by the melting and solidification of the powder. Fig 57a illustrates how pores are distributed along the length of the powder melt track while Fig 57b shows their distribution by depth. Most of the porosity is concentrated at depths between 0.1-0.3 mm which is shallower compared to the solid melt tracks. The maximum depth of observed porosity was around 0.4 mm closely matching the total melt pool depth of 0.39 mm for the continuous 250W powder melt track as shown in Table 5. In Figure 57b, a blue dotted line marks the interface between the bead and the base plate with the porosity in the bead region displayed on the left side of the graph. In all the powder melt tracks discussed below, the line separating the powder from the base plate is used as the reference point / origin. 68 Figure 56: Spatial profile of the porosity a) the top view b) side view. The yellow box in the top corner indicates the region of study for porosity in this 250W continuous powder melt track. Figure 57: Number of pores a) per unit length b) per unit depth in the 250W continuous powder melt track. The dotted line marks the separation between the bead and the base plate and is considered as the origin. 69 Fig 56b provides an overview of the aspect ratio of the porosity within the powder melt track while Fig 58a shows that most pores are spherical with only a few irregular ones, much like what was observed in the solid melt track. Figure 58b illustrates the spatial distribution of the pores in relation to their aspect ratio offering a detailed 2D view of how the pores are spread across the melt track. In this figure, two dotted lines are present: the first marks the top of the base plate and the second line below represents the melt pool depth as observed through Keyence imaging. A red dot appears below this second line which may be an artifact rather than actual porosity, although this remains uncertain. Despite the larger melt pool volume observed in the powder melt track compared to the solid track, the volume fraction of porosity is lower in the powder case. Figure 58: a) Number of pores per aspect ratio b) 2D representation of aspect ratio as a function of length and depth for the 250W continuous powder melt track. The first dotted line marks the separation between the powder and the base plate and is considered as the origin while the second dotted line represents the melt pool depth as measured by the Keyence microscope. 70 Fig 59a presents a cross-sectional view of pore sizes along the melt track. There are a few pores with a Feret diameter of around 0.20 mm, though many of them range between 0.02 and 0.09 mm. In short, there is not a significant difference in porosity reduction between the continuous solid and powder melt tracks under similar conditions. With this understanding, the focus shifts to examining whether pulsing offers any advantage in reducing porosity. Figure 59: a) Cross section of the 250W powder melt track showing variation of feret diameter b) Number of pores per feret diameter. A pulsing frequency of 500Hz at a 50% duty cycle was selected which would have an equivalent 250W continuous power to assess whether pulsing influences porosity formation. A side-by-side comparison was performed for both solid and powder melt tracks under these conditions. Figure 60 shows the spatial profile of porosity in the pulsed 500Hz and 50% duty cycle solid melt track. Compared to the continuous melt track, an increase in porosity is observed. The pores vary in size and shape. The larger pores are typically found near the top surface while the deeper ones are irregular and elongated, likely resulting from unstable keyhole collapse when the laser is turned off. These findings align with the observations in [96]. Furthermore, as shown in Figure 60a, the top view reveals porosity beneath the surface undulations indicating a pattern that could be linked to the formation of subsurface pores. 71 Figure 60: Spatial profile of the porosity a) the top view b) side view. The yellow box in the top corner indicates the region of study for porosity in this 500Hz & 50%DC solid melt track. Figure 61: Number of pores a) per unit length and b) per unit depth for the 500Hz & 50%DC solid melt track. 72 Figure 62: a) Number of pores per aspect ratio b) 2D representation of aspect ratio as a function of length and depth for the pulsed 500Hz & 50%DC solid melt track. Figure 61a reveals a distinct pattern in porosity formation during pulsing which appears to validate the earlier observation of porosity forming when the laser is turned off. Focusing on the 500Hz & 50% duty cycle case, where each cycle lasts 0.002 seconds, the laser beam travels roughly 0.1mm within that time. This suggests that porosity may form at regular intervals of 0.1mm and the data in Figure 61a supports this observation showing porosity at these intervals. To further explore this hypothesis, the same analysis was applied to the powder melt track at 500Hz & 50% duty cycle as seen in Figure 65. The consistent pattern in both cases points to a recurring phenomenon during pulsing that contributes to porosity formation. Figure 61b highlights the depth distribution of these pores with most occurring between 0.3mm and 1mm. Figure 62a reveals a distinct pattern compared to the continuous solid melt track. In the pulsed case, two distinct ranges of pores are observed. One set with low aspect ratio indicating more spherical pores and another with a high aspect ratio representing irregular, sharp-edged keyhole pores. Figure 62b further illustrates the spatial distribution of these pores in relation to their aspect ratios, offering a detailed 2D view of how they are 73 spread across the melt track. The maximum depth of porosity observed was around 1.1 mm which closely aligns with the total melt pool depth of 1.17 mm for the 500Hz & 50%DC solid melt track as shown in Table 5. Figure 62 illustrates the size of the porosity in the pulsed 500Hz & 50% duty cycle solid melt track. A few relatively large, nearly spherical pores are concentrated near the top surface with the largest pore having a feret diameter of 0.22 mm. As the depth increases, the pores become more irregular. This irregularity arises because at the end of each pulse, when the beam turns off, the keyhole collapses often generating bubbles in the melt pool. These bubbles can become trapped by the solidification front leading to the formation of irregular pores deeper in the material. Figure 63: a) Cross section of the 500Hz & 50%DC pulsed solid melt track showing variation of feret diameter b) Number of pores per feret diameter. Figure 64 shows the spatial distribution of porosity in the pulsed 500Hz & 50%DC powder melt track. In Fig 64b, porosity appears to be concentrated in the upper region at the end of the melt track which is typically observed under run-out conditions. However, during the quantification process, any porosity associated with run-out conditions was excluded from the analysis. This ensures that the porosity evaluated pertains only to the steady-state portion of the melt track and avoids transient effects seen near the end of the track. Compared to 74 the solid melt track, the powder track exhibits higher porosity with some observed in the bead. Figure 61a supports the hypothesis that porosity forms at regular intervals in both the pulsed solid melt tracks when the laser is turned off. When examining the powder melt track in Figure 65a, the porosity pattern is not clearly visible like the solid case especially near the beginning. So, the cumulative number of pores along the length was plotted as shown in Figure 66. The steps represent the end of each pulse occurring every 0.1mm. This confirmed that the porosity in the powder melt track also forms in regular intervals, validating the hypothesis for both cases and the cumulative porosity in the powder track is nearly double that of the solid one. Figure 64: Spatial profile of the porosity a) the top view b) side view. The yellow box in the top corner indicates the region of study for porosity in this 500Hz & 50%DC powder melt track. Figure 65b further illustrates the depth distribution with most pores occurring between 0.1mm and 0.8mm. Unlike the solid case, where most porosity was concentrated above 0.4mm, the powder melt track shows a more uniform distribution throughout the depth. 75 Figure 65:Number of pores a) per unit length and b) per unit depth for 500Hz & 50%DC powder melt track. Fig. 67 compares the cumulative number of pores between the pulsed 500Hz-50%DC and the corresponding average power (continuous 250W) solid melt tracks. Pulsing increased the porosity by almost 33% when compared to the continuous 250W melt track. This is due to the laser being turned off between pulses, which causes keyhole instability and leads to the entrapment of gas. Figure 68a shows that the powder melt track developed larger pores compared to the pulsed solid melt track. Similar to the solid track, two distinct ranges of pores are observed: one with a low aspect ratio indicating more spherical pores and another with a high aspect ratio representing irregular, sharp-edged keyhole pores. Figure 68b further visualises the spatial distribution of these pores in relation to their aspect ratios, offering a detailed 2D view of how they are spread across the melt track. The maximum depth of observed porosity was around 0.82 mm suggesting that the cross-section captured during the Keyence observation and its data in Table 5 did not correspond to a steady-state melt pool depth for this condition. 76 Figure 66: Cumulative number of pores across 500Hz & 50%DC solid and powder melt track. The solid melt track is represented by blue and the green represents the powder melt track. The steps represent the occurrence of each pulse, occurring at intervals of 0.1 mm. Figure 67: Cumulative number of pores across pulsed 500Hz & 50% DC and continuous 250W solid melt tracks. The pulsed melt track is shown in blue and the continuous 250W melt track is represented in green. Figure 68 illustrates the size distribution of porosity in the pulsed 500Hz & 50% duty cycle melt track. A few relatively large, nearly spherical pores are concentrated near the top surface with the largest having a feret diameter of 0.17 mm. In contrast to the solid melt track, some large pores are also found at greater depths in the powder melt track indicating that powder keyholes are more unstable than solid ones. They were likely trapped because they had insufficient time to rise and escape from the melt pool due to the rapid collapse and solidification of the keyhole. 77 Figure 68: a) Number of pores per aspect ratio and b) 2D representation of aspect ratio as a function of length and depth pulsed 500Hz & 50%DC powder melt track. The dotted line marks the separation between the powder and the base plate and is considered as the origin. 78 Figure 69:a) Cross section of the 500Hz & 50%DC pulsed powder melt track showing variation of feret diameter b) Number of pores per feret diameter. 5.3 Summary This chapter presents two sets of experimental results. The size and shape of melt pools created in both solid and powder Ti-6Al-4V were quantified for both pulsed and continuous laser beams. This was followed by cross-sectional measurements of the melt pool dimensions and a detailed characterization of the porosity induced in selected pulsed and non-pulsed conditions. Key findings include that the melt track widths generally increase with higher power levels and powder melt tracks exhibit narrower widths and rougher surfaces compared to their solid counterparts. For pulsed samples, at lower duty cycles, intermediate pulsing frequencies produced wider melt tracks compared to both higher frequencies and continuous conditions. Cross-sectional imaging of the melt tracks confirms that most of the tracks were produced under keyholing conditions. Pulsing was found to have a detrimental effect on porosity formation in the samples. As seen in Fig. 67, pulsed conditions led to almost 33% more porosity compared to continuous melt tracks with the same average power. The porosity in continuous melt tracks did not exhibit any clear spatial pattern but pulsed melt tracks exhibited a more distinct and patterned porosity distribution. This is attributed to the laser turning off between pulses, which causes keyhole instability and leads to the entrapment of gas. 79 Chapter 6 – Simulations to understand melt pool dimensions and porosity formation In this chapter, two different models will be used to try to rationalise some of the above observations. First, the thermal only FAST model (FM) coupled with a phenomenological model for predicting melt pool dimensions in keyholing will be used to understand how melt pool size varies as a function of the processing conditions (pulsing and continuous). It's important to acknowledge that the FAST model, like any thermal-only model, cannot accurately predict melt pool dimensions under keyholing conditions that mainly occur here. Despite this, ways in which the FAST model can be used to make estimations of melt pool size when coupled with existing phenomenological models from the literature will be highlighted. Another key observation in this study is the correlation between porosity and pulsing in pulsed laser melt tracks. Preliminary fluid dynamics simulations will be used to shed light on the origins of this porosity. These simulations are designed to go further than what FAST model can predict, exploring how fluid flow can influence melt pool geometry and to understand the mechanism behind porosity formation during pulsing. 6.1 Rationalising the observation of keyholing As discussed in the previous chapter, the process conditions in our experiments primarily result in keyholing, except at the lowest duty cycles and frequencies. This is corroborated with the work from [64,173], who used aspect ratios of the melt pool to classify different regimes. According to their classification, conduction mode produces melt pools with aspect ratios below 0.5, transition mode ranges between 0.5 - 0.8 and keyhole mode is identified when the aspect ratios exceed 0.8. For instance, the 125W continuous melt track in our study is in the transition regime. There are also PV process maps [44,84] for Ti-6Al-4V that predict the specific regime (keyholing, transition, or conduction) based on the selected processing parameters. AM processes generally operate at lower power levels (typically in the range of watts) and higher speeds (typically in the range of m/s) compared to welding which uses higher power levels (kW range) and lower speeds (typically mm/s) [174,175]. According to Zhao et al. [70], operating in a stable keyhole regime allows for efficient melting even at lower energy inputs and a wider process window that offers flexibility in adjusting parameters while maintaining a consistent melt pool. This could be key to optimising production rates, as it enables the use of thicker powder layers without sacrificing quality. The powder used in this process is larger than typically used in SLM [175] but similar to the powder size used in EBAM [176] with a layer thickness of around 200µm. The rationale behind this is that even though we are operating at a slower speed, the thicker layer aims to achieve productivity levels comparable to using a thinner layer at higher speeds, balancing the 80 trade-off between speed and efficiency. 6.2 Prediction of melt pool dimensions using FAST model As outlined in Chapter 4.4, the focus here is to explore whether the FAST model could be used to predict melt pool geometry. Observing Fig 39, it is evident that the experimental melt pool widths for both solid and powder tracks were nearly identical except at lower power levels like 125W. Therefore, the FAST model was primarily used to compare the predicted melt pool widths with the experimental data from the solid melt tracks. The FAST model can’t predict keyholing but the intent is to show that it can still predict melt pool width. This limitation arises because the model was built for operating in AM conditions where keyholing is not common. To get more accurate depth predictions, the model would require modifications such as incorporating an additional heat source beneath the primary source [177], adjusting boundary conditions or integrating a keyhole model to better align with experimental data [151]. The FAST model is still a work in progress in addressing these challenges. 6.2.1 Prediction of melt pool dimensions using FAST model: Continuous melt track As shown in Fig 70, the FAST model was used to predict melt pool dimensions including both depth and width and the results were compared against experimental cross-sections. The FAST model performed well in predicting melt pool width using a single value of energy absorption efficiency across all conditions, without any other tuning parameters, although it struggled with estimating melt pool depth. However, it provides a reasonable approximation for the upper portion of the keyhole’s depth and shape. Figure 70: The melt pool geometry created using results from FAST model superimposed on the experimental cross sections for various power levels. 81 The light blue represents the liquid melt pool, which is the region where the material is in a molten state. The purple line indicates the solidus line, which is the temperature at which the material begins to solidify as it cools. It marks the boundary between the fully liquid and partially solid regions of the melt pool. Figure 71 presents the melt pool width variation comparing FAST model (FM) predictions with experimental results at various powers and absorption efficiencies of 0.4, 0.45 and 0.5. In addition, the model assumes a beam with a Gaussian shape and standard deviation of 0.145mm. The heat loss due to radiation was considered with an emissivity of 0.7. The FAST model effectively predicts the melt pool width with the 0.45 efficiency showing closest match to experimental results among the three efficiencies evaluated for all samples except the one produced at 500W. In this case, the model over-predicts the melt pool size significantly. As noted above, there are two other assumed parameters in the model, the beam size and the emissivity. It has been reported that high laser power densities can alter the optical path leading to a shift in the focal point due to thermal lensing [178]. Thus, to test the sensitivity of the results to the beam size, additional simulations were performed. Figure 71: Comparison of melt pool width variations predicted by the FAST model (FM) against experimental (EXP) results for continuous melt tracks in solid Ti-6Al-4V across various power levels and absorption efficiencies of 0.4, 0.45 and 0.5. The error bar (black) represents the maximum predicted power loss of up to 10%, indicating an actual output range between 450 to 500W. 82 FAST model predictions were performed again but this time using beam standard deviations of 100µm and 200µm (Fig 72). Each colour in the figure corresponds to a different efficiency (η) while the shaded regions represent the range of results based on different beam sizes. The darker symbols correspond to the standard beam size of 145µm. The results reveal a very narrow shaded region indicating that variation in beam size has negligible effect on the melt pool width predictions suggesting that beam size is not a significant factor in the over prediction of melt pool size at 500W. Figure 72: Comparison of melt pool width variations predicted by the FAST model against experimental results (black) across various power levels and absorption efficiencies (η) of 0.4, 0.45 and 0.5 for various beam size. The darker symbols (circles) correspond to the beam standard deviation of 145µm. The next plausible explanation considered was the effect of radiation. The FAST model was run again for no-radiation condition as well as for different values of emissivity (ϵ) to examine their effects on melt pool width as illustrated in Fig 73. The melt pool width is plotted against power for an efficiency of 0.45 with a thin shaded region representing different beam standard deviation (100-200µm) and the dark symbols corresponding to the 145µm beam standard deviation. Similar to the previous analysis, radiation was found to have minimal influence on the melt pool width for the tested conditions. The final potential cause for the mismatch in melt pool width predictions was suspected to be related to the experimental conditions, specifically the requested power. Considering that 500W is the maximum power 83 output for the SLM machine, discussions with the industrial partner revealed that full power at 500W may not have been consistently reached. As noted earlier, the actual beam power was not measured during these experiments and 500W is the maximum output for the system employed. This would explain the narrower melt pool width observed experimentally. Future tests are needed to measure the direct beam power at the requested 500W to verify this hypothesis. It is estimated that the maximum predicted power loss could be around 10%, indicating that the actual output could have ranged between 450 to 500W. Figure 71 includes a black error bar to account for the potential power variation, reflecting the possible range of actual output (450 to 500W) due to the estimated 10% power loss. Figure 73: Comparison of melt pool width variations predicted by the FAST model across various power levels for various beam standard deviation (100-200µm) for no radiation condition and for different emissivity (ϵ) values. Finally, it must be acknowledged that the FAST model is being used here to predict under conditions where there is keyholing and that complex fluid flow effects not considered in the FAST model may play an increasing role in the melt pool width as laser power increases. In this case, it may be that the deviations observed at 500W are caused by these phenomena. This will be revisited later in the chapter when the melt pool dimensions are predicted using a fully coupled thermal-fluid flow CFD model. 84 6.2.2 Prediction of melt pool dimensions using FAST model: Pulsed melt track In contrast to continuous melt tracks, superimposing the FAST model predictions onto experimental cross sections was not feasible for all conditions since some of these cross sections were not obtained under steady-state conditions, meaning they may not accurately represent the true shape of the melt pools. To address this, the melt pool width was compared by overlapping the experimental top surface of the melt tracks on the solid plate with predictions from the FAST model. The choice of simulating on the solid plate rather than with powder is to allow for the best comparison with the assumptions of the FAST model which does not explicitly account for powder. Figure 74: Comparison of the melt pool width predictions from FAST model superimposed onto the experimental cross sections of the top surface for selected pulsing conditions a) 10Hz-50%DC b) 10Hz-75%DC c) 100Hz-25%DC d) 100Hz-50%DC & e) 100Hz-75%DC. 85 As illustrated in Fig. 74, the FAST model provides a reasonable prediction of the melt pool width from the top view, capturing the nuances of pulsing across various pulsing conditions. For instance, at a frequency of 10Hz with a 50% duty cycle, each pulse lasts 100ms, with the laser being active for half of this duration (50ms). During this active period, the laser moves 2.5mm, forming a segment of the melt track. The laser then turns off for next 50ms leaving a 2.5mm gap where no melting occurs. When the laser is turned on again, it creates a new segment, resulting in a discontinuous melt track. At an intermediate frequency of 100Hz, each pulse (5ms) is getting turned off and on faster, leading to a continuous melt track as seen in Fig 74d. At higher frequencies, such as 10 kHz and 5 kHz, the laser pulses occur rapidly, leading to melt tracks that closely resemble those produced in continuous mode across all duty cycles. The FAST model predicts this melt track behaviour which is useful for optimising process parameters. A more careful look at the measured melt track widths, however, shows some deviations in these predictions. Figure 75 shows the experimentally measured melt track widths in the solid build plate for nine different conditions, corresponding to frequencies of 100 Hz, 500 Hz and 1000 Hz for each of the three duty cycles investigated. Referring back to the results from Chapter 5, that the melt tracks at frequencies above 1000 Hz, all exhibited almost same melt track widths while the melt tracks at frequencies below 100 Hz were fully disconnected. The intermediate frequencies were focused on for comparison with model results. The experimentally measured melt track widths are represented as black squares in each figure while the red and black dashed lines show the melt pool widths measured for continuous melt tracks at 500W and the average power corresponding to the duty cycle for the plotted data (e.g. 375W for 75% duty cycle) respectively. At zero frequency (assuming that the beam is on at the start of the cycle), the result would be the same as that for a continuous melt track at 500W. In contrast, when the frequency becomes very large, we expect that the conditions will tend towards those obtained by having a continuous beam with the effective average beam energy corresponding to the duty cycle. Looking at the experimental data, we see that all of the measured widths do appear to vary between these two limits. As pointed out in Chapter 5, melt tracks formed at low duty cycle and low frequency (100Hz and 25%DC) exhibit significantly wider melt tracks compared to those at 500Hz and 1000Hz for the same duty cycle. The gradual transition from the upper limit to lower limit could be attributed to the need to accurately calculate the average beam power within these frequency ranges. In general, the beam interacts with a point on the surface for a time 𝑡𝑖 = 𝜎𝑉𝑠 where σ is the beam’s standard deviation and 𝑉𝑠 is the beam’s velocity. In this time, the beam will be on for 𝑛 = 𝑡𝑖 ∗ 𝑓 or 𝑛 = 𝑡𝑖𝑇 cycles, where f is the frequency, T is the period ( 1𝑓 ). 86 Figure 75:(a-c) Variation in melt pool width predicted from average beam power calculations and experimental for three different frequencies (100Hz,500Hz and 1000Hz) across three different duty cycles (a) 25%, (b) 50% and (c) 75%. The blue solid line represents the predicted melt pool width based on the average power. (d-f) Variation in melt pool width predicted by the FAST model and experimental for three different frequencies (100Hz,500Hz and 1000Hz) across three different duty cycles (d) 25%, (e) 50% and (f) 75%. The dashed lines correspond to the average continuous power according to the duty cycle (black) and the continuous 500W melt track (red). The blue shows the average power delivered to the material at a point. 87 This value is typically not an integer, meaning that at low frequencies, some part of the average power comes from full on/off cycles of the beam, while the rest from a partial contribution from an on/off cycle. The number of full cycles is computed as 𝑛𝑖 = 𝑖𝑛𝑡 (𝑡𝑖𝑇) and during each of these cycles, the average power is P*DC, where P is the amplitude of the power and DC is the duty cycle. Therefore, the contribution to the average power to a point is given by 𝑛𝑖 ∗ 𝑃 ∗ 𝐷𝐶. The remaining partial cycle occurs over a time 𝑡𝑝 = 𝑡𝑖 − 𝑛 ∗ 𝑇. Assuming that in this time the beam is on, the contribution from this partial cycle is 𝑡𝑝𝑡𝑖∗ 𝑃 and the average power in this case would be 𝑃𝑎𝑣𝑒 = 𝑛𝑖 ∗ 𝑃 ∗ 𝐷𝐶 +𝑡𝑝𝑡𝑖∗ 𝑃. If the residual final portion of the average includes some time when the beam is off, then this has to be corrected for by reducing 𝑡𝑝 to the time when the beam is actually on. This allows us to obtain an estimate of the average power delivered to a point as a function of the beam pulsing conditions. If we then use the data in Figs. 71 or 72 to make a correlation between average beam power and melt pool width, one could predict the melt pool width as shown as the blue solid line in Fig. 75. As expected, the predicted melt pool width varies as a function of frequency between the upper and lower limits described above but continuously varies between them. The prediction shows oscillations which are a direct result of averaging over the final on/off cycle of the beam. In the limit, where there are only integer number of cycles in the time 𝜎𝑉𝑠 then the average power is just given as the amplitude of the power times the duty cycle (i.e. the lower black dashed line in each plot). One can see that this simple prediction does capture general trends correctly, showing that the melt pool width (particularly for 25% and 50% duty cycle) is expected to decrease as frequency increases. However, this prediction has some significant approximations which makes it unlikely to exactly predict the experimental results. First, the method assumes that the beam energy is either fully on at 500W or fully off but the beam is expected to have an energy distribution (i.e. Gaussian shape). Also, by correlating the average power calculated in this way to the data in Figs. 71 or 72, it is assumed that the steady state melt tracks are a good representation of what happens when pulsing occurs. This ignores the fact that in pulsing, particularly at lower frequencies, the melt pool will shrink (and possibly disappear) during the off cycle and will have to be re-formed during the on-cycle. Achieving the steady state melt pool size at a given power will takes time, so it is likely that using the average power calculation correlated to the continuous melt track widths over predicts the melt pool size at low frequency and duty cycles. There does appear to be some evidence of this in the results. Finally, the FAST model was also tested to predict the melt pool widths under pulsing conditions. This turned out to be a significant challenge for the FAST model particularly at higher pulsing frequencies where a very fine time step was required to be able to resolve the melt pool size with high accuracy. The results of the 88 FAST model predictions, compared to the experimental results are shown in Fig. 75 (d,e,f). In this case, it is observed that the FAST model predicts a decrease in melt pool width with increasing frequency for a 25% duty cycle but shows a frequency-independent response across the tested frequencies for duty cycles of 50% and 75%. At 100Hz, the predicted melt pool size is significantly lower than that predicted from the power averaging method described above and this can be attributed to the fact that the time that the beam is on, is insufficient for the melt pool to reach a steady state size. At higher frequencies, the FAST model tends to overpredict the melt pool width. This suggests that the beam oscillation is predicted to be fast enough that the melt pool does not shrink significantly during the off cycle. This allows the melt pool to grow to a steady state size that is between the upper and lower limits (dashed lines). However, it should be noted that significant variation in melt track width was predicted by the FAST model at 1000Hz, indicating that the integration scheme used by the model may be inadequate to adequately capture the high pulsing frequencies. Further work would be needed to validate the FAST model under such conditions. Finally, despite the simple nature of the FAST model, it appears to do a reasonably good job of predicting melt pool width even in cases where the conditions lead to keyholing. To understand why the FAST model is able to adequately predict the melt pool width even though the experiments are conducted in keyholing conditions, it is important to first consider the factors that govern its behaviour. When a metal surface is heated with a laser beam. The impinged area will experience the most intense heat. The heat spreads out into the surrounding material through thermal conduction. The extent of this lateral heat spread is governed by the material’s thermal conductivity and thermal diffusivity which in turn determines the melt pool width [179]. As a result, the surface heat distribution and the thermal gradients near the surface are more predictable and easier to model. The FAST model works well for predicting the width of the melt pool in both pulsed and non-pulsed conditions (Fig 70 and 74), as it accounts for the time-dependent conduction effects (i.e., how heat spreads over time due to conduction) which enables the model to estimate melt pool expansion from thermal accumulation. The use of temperature-dependent material properties allows the model to account for the fact that heat conduction changes as the material heats up, rather than assuming a constant heat transfer rate throughout the process. In their studies, Han et al. [180] demonstrated that the melt pool width is influenced by two key factors: the width of the laser beam and the rate of heat loss during the cooling phase of the weld area. Additionally, they observed that the melt pool width increases as the temperature and enthalpy of the heated region rise. 89 6.2.3 Integrating FAST model with phenomenological model for melt pool depth predictions Although the FAST model is not designed to predict melt pool depth in keyholing conditions, some literature [73,183] points out that more accurate depth predictions could be achieved with using phenomenological models. In this section, an attempt is made to see whether depth predictions could be made using a combination of this empirical approach in conjunction with the FAST model. The focus here is specifically on predicting the keyholing for the continuous melt tracks. Hann et al [180] showed how various process parameters affect the melt pool depth (MPD) for different materials in laser welding. They showed that the melt pool depth is influenced by a combination of the laser parameters, material properties and thermodynamic factors. Gan et al. [73] proposed a phenomenological model that defines a dimensionless number called the Keyhole number (Ke) and its relation to aspect ratio (e*). The Keyhole number and the aspect ratio are given by 𝐾𝑒 =𝜂𝑃(𝑇1 − 𝑇0)𝜋𝜌𝐶𝑝√𝛼𝑉𝑠𝑟03 𝑒∗ = 0.4(𝐾𝑒 − 1.4) The keyhole parameter is represented in several different ways in the literature but the final form of the equation is the same. According to [181] this model is based on two key dimensionless parameters: the ratio of dwell time to thermal diffusion time and the dimensionless laser intensity. Instead, [180, 181] offer an alternative scaling, where the keyhole parameter is expressed as the ratio of specific enthalpy (ΔH) to enthalpy of melting (ℎ𝑠). For simplicity, let's focus on the latter approach. When a laser with power P is irradiated on an area of 𝜋𝜎02 for a duration defined by the dwell time τ which is approximately 2𝜎0𝑉𝑠 ,where 𝜎0 represents the beam standard deviation and 𝑉𝑠 denotes the scan speed. The heat diffuses from the surface into the material into a depth of approximately 2√ατ [182], where α is the material's thermal diffusivity. The material absorbs energy during the dwell time which is given by 𝜂. 𝑃. 𝜏, where η is the efficiency factor (fraction of energy absorbed by the material). This absorbed energy is distributed across a volume of 𝜋𝜎022√𝛼𝜏. Combining all these factors, specific enthalpy ∆𝐻 can be expressed as: ∆𝐻 =𝐸𝑎𝑏𝑠𝑜𝑟𝑏𝑒𝑑𝑉𝑎𝑓𝑓𝑒𝑐𝑡𝑒𝑑=𝜂𝑃𝜏2𝜋𝜎02√𝛼𝜏=𝜂𝑃.2𝜎0𝑉𝑠2𝜋𝜎02√𝛼.2𝜎0𝑉𝑠=𝜂𝑃𝜋√2𝛼𝑉𝑠𝜎03 90 The enthalpy of melting (ℎ𝑠) quantifies the heat required to transform a unit volume of material from solid to liquid at its melting temperature. It is given by: ℎ𝑠 = 𝜌. 𝐶𝑝. (𝑇1 − 𝑇0) where 𝐶𝑝 denotes the specific heat capacity, 𝑇1 is liquidus temperature and 𝑇0 represents the initial temperature. When the specific enthalpy is divided by the enthalpy of melting, it gives a dimensionless enthalpy which is also referred to as the Keyhole number given by. 𝐾𝑒 =𝛥𝐻ℎ𝑠=𝜂𝑃(𝑇1 − 𝑇0)𝜋𝜌𝐶𝑝√2𝛼𝑉𝑠𝜎03 𝑒∗ = 0.4(𝐾𝑒 − 1.4) The Keyhole number offers two main advantages: it accounts for material properties which allows it to describe melt pool behaviour across different materials. Its dimensionless nature enables comparison of processes independent of specific parameter values. The values of the parameters used for calculation of the keyhole parameter are listed in Table 2. In their studies [73,183], the aspect ratio refers to the ratio of the depth to the width of the vapour depression rather than the melt pool. However, measuring the vapour depression directly in our experiments is not feasible. Therefore, the aspect ratio in this study is defined as the melt pool depth divided by the melt pool width. Since the Keyhole number is directly proportional to the power, aspect ratio was plotted against power to examine how well this empirical correlation holds. Fig. 76 shows the variation of aspect ratio as a function of power for 0.45 efficiency. The choice of this efficiency value stems from our observation that the efficiency that replicates results closest to the experimental data lies between them as shown in Fig 71. The black circles represent the aspect ratios calculated from experimental data. The shaded area indicates the range of beam standard deviation, from 100µm to 200µm. The upper and lower faded circles in the shaded region denotes 100µm and 200µm respectively. The darker green circles connected by a solid line in the centre correspond to the beam standard deviation of 145µm used in the experiments. The results show that the empirical approach performs quite well in predicting the aspect ratio except at higher power (500W). This inconsistency may be partly attributed to the way the aspect ratio was calculated in this study. The phenomenological model does not account for latent heat, fluid flow and the effects of material vaporisation, where some of the material's mass and enthalpy escape as vapour. As it doesn't account for these physics, it is expected that the results could agree with the experimental results in the conduction-melting regime but not necessarily in the deep keyhole regime. 91 Figure 76: Variation of aspect ratio as a function of the power across an absorption efficiency of 0.45 for various beam standard deviation (100-200µm). The darker symbols ( green circles) correspond to the beam standard deviation of 145µm. The black circles indicate the results from experiments. The error bar (black) represents the maximum predicted power loss of up to 10%, indicating an actual output range between 450 to 500W. Gan et al [73] found that despite variations in material properties and processing conditions, when the aspect ratio is plotted as a function of the keyhole number, they all collapsed into a single curve as seen in Fig 77a. In Fig 77b, we attempted to verify the applicability of this universal scaling law in our case and whether one could predict if the process operates in transition or keyhole mode based on the processing conditions. For instance, the plot shows that for a 145µm beam size both the 375W and 500W melt tracks fall in the keyhole regime which is consistent with our experimental observations. The core idea is to see whether one could potentially calculate the melt pool depth using this aspect ratio and integrate it into the FAST model to obtain a more accurate estimation of the melt pool depth (MPD). Although the aspect ratio in our experiments was measured differently compared to the methodology used in [73,182], this approach provides a better estimate of the melt pool depth (Fig. 79b) when used in conjunction with the FAST model for predicting MPD in keyholing conditions. 92 Figure 77: a) Keyhole aspect ratio as a function of keyhole number for various materials and processing conditions showing a unified trend despite variations in material properties and processing parameters [73]. b) Aspect ratio is plotted as a function of keyhole number for Ti-6Al-4V under various experimental conditions with different beam standard deviations and power, exhibits the same trend and collapses into a single curve. 6.3 Prediction of melt pool size and porosity formation using CFD modelling The objective here is to predict the aspects that the FAST model cannot capture. Even though the CFD model operates with several assumptions and simplifications, the focus is not on precise quantification of melt pool dimensions but more on understanding the fluid flow phenomena that the FAST model does not account for. CFD provides higher fidelity predictions than the FAST model by accounting for fluid flow but this accuracy comes at a significant computational cost, restricting these simulations to much smaller length and time scales than those required to model an entire part. Despite these limitations, CFD is a valuable tool for understanding how gas porosity forms and becomes trapped during keyhole formation. It provides detailed insights into the fluid dynamics including the motion of the melt pool, keyhole instability and the role of vapour pressure in trapping gas bubbles. 93 6.3.1 Prediction of melt pool dimensions using CFD model for continuous melt tracks As seen in Fig. 78, the CFD model was used to predict melt pool dimensions (depth and width) and these predictions were compared with experimental cross-sections. The superimposed purple line represents the outer profile of the melt pool as predicted by the CFD model. The CFD model correctly predicts the occurrence of keyholing for all power levels except the lowest which aligns with the experimental observations. Across all the power levels, the model underpredicted the melt pool dimensions. This reveals some limitations in the CFD model and underscores the necessity to incorporate additional physical factors to improve the model's accuracy. Figure 78: The melt pool geometry created using results from CFD model superimposed on the experimental cross sections for various power levels. Figure 79 presents the melt pool width and depth as a function of power. The FAST model performs well in accurately predicting the melt pool width while the CFD model tends to underpredict indicating potential shortcomings in capturing certain physical phenomena. Also notable is that, for the higher powers, a linear trend between melt pool width and power is observed, this being parallel to the FAST model predictions. Earlier it was suggested that the deviation observed experimentally at 500W could be due to fluid flow effects not considered in the FAST model particularly under high energy densities and keyholing. However, this does not seem to be the origin of the results observed here. When it comes to melt pool depth, the CFD model outperforms the FAST model which made inaccurate predictions due to reasons discussed earlier. The CFD model still performs reasonably well given its limitations. Several factors contribute to this and one possibility is that the constant material properties used in the CFD model differs from those used in the FAST model. For instance, the thermal conductivity in the FAST model was 0.34W/cm.K while the CFD model used 0.38W/cm.K, which could account for the smaller predicted melt pool depth in the CFD model. The CFD domain has fixed insulated (zero flux) boundaries, limiting heat dissipation. This can affect the results by 94 preventing heat loss, potentially leading to unrealistic temperature profiles and inaccurate predictions of melt pool behaviour. Figure 79: a) Variation of melt pool width as a function of power for experimental, FAST and CFD models b) Variation of melt pool depth as a function of power for experimental, FAST, empirical approach and CFD models. The error bar (black) represents the maximum predicted power loss of up to 10%, indicating an actual output range between 450 to 500W. 95 6.3.2 Prediction of porosity formation using CFD model: Continuous and Pulsed melt tracks In this section, we aim to explore whether computational fluid dynamics (CFD) simulations can help explain some of the observations seen earlier. First, micro-computed tomography (μCT) analysis revealed that pulsing increases porosity by 33% compared to the same average power for a continuous melt track. This raises the question of why pulsing, despite the same energy input leads to more porosity. Second, μCT imaging also showed that porosity forms consistently at the end of each pulse, raising the question of whether CFD can provide insight into the physical processes contributing to this behaviour. This section aims to enhance the understanding of how pulsing influences melt pool dynamics and porosity formation. As seen in Fig. 80, although the melt pool dimensions vary, the general structure of the melt pool and the formation of the keyhole are consistent across all three continuous melt tracks. In these figures, the white line represents the boundary between the gas and liquid phases while the blue line separates the solid from the liquid. The images are shown as a cross-section through the centre of the melt track with the direction of beam travel being to the right. In each case, the images have been taken after similar amounts of simulation time where near steady-state conditions are believed to have been obtained. It is in these conditions that the melt pool dimensions reported in Fig. 79 have been obtained. Figure 80: A single time frame illustrating melt pool shape and formation of keyhole in a continuous (a) 250W, (b) 375W and (c) 500W melt track. The white line represents the boundary between the gas and liquid phases while the blue line separates the solid from the liquid. Given the similarity of all three continuous melt tracks, let us examine one in more detail. Figure 81 illustrates the melt pool evolution and the formation of gas bubbles in a continuous 375W melt track. The white line represents the boundary between the gas and liquid phases while the blue line separates the solid from the liquid. In Figure 81 (a & b), the melt pool progresses into a keyhole allowing vapour to escape. Occasionally, 96 as seen in Figure 81 (c-e), the tip of the keyhole swells forming a gas bubble which is likely due to intense evaporation occurring at the right-side bottom of the keyhole [183]. These bubbles tend to rise from the tip of the keyhole to the rear of the melt pool with some escaping at the surface or drawn back into the keyhole like 81 (f). However, bubbles that are near the solidifying side walls are more likely to be trapped forming porosity. Figure 81: Melt pool evolution and the formation of gas bubbles in a continuous 375W melt track. The white line represents the boundary between the gas and liquid phases while the blue line separates the solid from the liquid. Fig 82 illustrates the melt pool evolution and the formation of porosity in pulsed 500Hz and 50%DC melt track. For the above conditions, each cycle (where the laser is on and off) lasts approximately 2ms. Here the blue line represents the boundary between the gas and liquid phases while the white line separates the solid from the liquid. In (a), the laser is on, energy gets inputted into the material creating a deep keyhole. A preexisting porosity from an earlier cycle can be seen here. As the keyhole progresses, it remelts the surrounding material and reabsorbs the porosity into the keyhole as seen in (b). During the laser-off phase (e-g), rapid solidification occurs, particularly at the bottom of the keyhole resulting in a clear separation between the keyhole and the bubble which gets entrapped as porosity. This behaviour aligns with findings from welding [183], where intense evaporation at the keyhole's bottom induces strong liquid flow carrying bubbles away from the keyhole. However, bubbles that are near the solidifying side walls are more likely to be trapped forming porosity. This 97 phenomenon also explains the reduction in porosity at lower speeds in welding, where bubbles have more time to escape or be reabsorbed into the keyhole before the material solidifies. In (h), the laser is turned back on and proceeds to form a keyhole and the process repeats. A key difference that can be observed between Fig.81 and Fig. 82 is the width of the keyhole. In the case of pulsing, the keyhole width is smaller compared to the continuous melt track. This is due to the intermittent nature of the laser energy. When the laser is on during a pulse, it delivers a high energy input in a short amount of time, causing rapid melting and vaporization of material which forms the keyhole. When the laser is turned off, the keyhole begins to shrink due to solidification and trap bubbles, so the melt pool does not have enough time to expand fully. As a result, the keyhole remains narrow. In contrast, the continuous melt track with the same average power maintains a constant keyhole width due to the continuous energy input. Figure 82: Melt pool evolution and the formation of porosity in pulsed 500Hz and 50%DC melt track. The blue line represents the boundary between the gas and liquid phases while the white line separates the solid from the liquid. However, pulsing has been shown to reduce porosity in laser welding across several studies [184,185], a result that can be attributed to the operational differences between welding and AM processes. The slower speeds in welding allow the melt pool to remain in a liquid state for a longer duration preventing full solidification between pulses. When the pulse overlap is sufficiently high, the solidified metal around pores from earlier pulses is repeatedly remelted by subsequent pulses which allows trapped gas bubbles more time to 98 escape. Previous research has investigated laser pulsing in the LPBF process with aluminium alloy [96]. However, the melt pool dimensions observed in those studies did not exhibit the same level of keyholing and the porosity levels were not as pronounced in the present study. This difference can be attributed to aluminium’s higher thermal conductivity compared to materials like Ti-6Al-4V as well as the lower VED values used in those studies relative to the current work. Figure 83: Illustration of porosity formation in keyholing conditions during laser welding. The red region represents the melt pool while the blue region depicts the solidified weld containing entrapped porosity. Figure redrawn and adapted from [183]. When viewed in cross-section (Fig 51), the powder samples exhibit larger melted volumes compared to those formed from the solid ones under the same process conditions. As mentioned earlier, some of these cross-sections may not represent steady-state conditions and may not accurately reflect the actual melt depth. The cross-sectional analysis of the samples reveals that pulsing results in higher levels of porosity within the samples rather than mitigating it. Recent studies have shed light on the mechanisms by which porosity becomes entrapped during solidification processes [96,184]. The keyhole displays a dynamic behaviour fluctuating in size and depth. Two distinct liquid flows are present: one circulates around the keyhole towards the rear of the melt pool while the other moves downward through the keyhole as seen in Fig 83 [183]. Another stream that is induced by the intense evaporation carries bubbles along with the liquid from the base of the keyhole to the upper rear surface of the melt pool. In some instances, this evaporation causes the keyhole tip to swell, forming bubbles and widening the base. These bubbles can rise from the keyhole tip toward the surface of the molten pool with some escaping while others become trapped against the solidifying front resulting in the formation of porosity. 99 Another example illustrating porosity entrapment during laser pulsing in LPBF is depicted in Fig 84 [96], which has also been simulated by a model [185]. At lower frequencies, when the laser is active, it creates a keyhole with vapour depression. As the laser turns off, solidification of the melt pool happens especially at the base and starts to form a cavity. When the laser reactivates, a new melt pool forms and connects with the cavity from the previous pulse (d). As input energy increases, the melt zone deepens causing some upper portions of the cavity to fill while the bottom portion remains as a closed pore within the material (e-f). If the overlap between successive melt pools is insufficient or the scanning speed is too high, the porosity remains trapped. This process repeats leading to a pattern of pores in the substrate which is also reflected in our findings. Gao et al. [145] demonstrated that pulsing induces a stirring action within the melt pool which helps gas bubbles move. Larger bubbles tend to rise to the surface due to this stirring motion while smaller ones may still become trapped by the solidifying front. Zhao et al. [167] explained how the velocity distribution in the melt pool during keyhole collapse influences bubble movement. Smaller bubbles tend to move horizontally because the higher liquid velocity below them creates a pressure difference preventing them from rising and causing them to become trapped as the material solidifies. On the other hand, larger bubbles due to their increased buoyancy are more likely to rise and escape resulting in larger pores near the surface. The smaller ones remain trapped forming keyhole pores. This phenomenon was also observed in [186]. Figure 84: Sequential stages of laser pulsing in LPBF of Ti-6Al-4V, illustrating porosity formation [96]. The CFD model and existing literature help to explain why porosity is higher under pulsing conditions compared to continuous melt tracks. When the laser is turned off, rapid solidification occurs, leading to a narrowing of the keyhole which prevents the bubbles from rising to the surface in time to escape. The faster solidification rate at the tip of the keyhole or the advancing solidifying front entraps these bubbles resulting in higher porosity. When the laser is turned back on, there is less chance that the previously formed porosity will be remelted or reabsorbed into the melt pool. In contrast, for a continuous melt track, the continuous energy input keeps the keyhole open for longer time, resulting in a slower solidification rate and allowing the bubbles 100 more time to escape, thereby reducing the amount of porosity. 6.4 Summary This chapter explored the potential of using simulations to predict melt pool geometry and understand porosity formation across different processing regimes for both continuous and pulsed laser melt tracks. The FAST model performed well in predicting melt pool width but it struggled with melt pool depth predictions, particularly in keyholing scenarios. This limitation stems from its inability to account for the complex dynamics of fluid flow, recoil pressure and material vaporisation that occur during keyhole formation. However, by integrating the FAST model with phenomenological model can improve understanding of melt pool geometry but further refinement is essential. The CFD model proved especially effective at capturing the trends observed in the experiments for melt pool geometry during keyholing, although not with exact quantification as it accounted for fluid flow. The chapter also investigated the formation of porosity in the melt pool under keyholing conditions. In continuous melt tracks, gas bubbles have more time to escape due to a stable keyhole that does not collapse along with slower solidification rates. In pulsed melt tracks, the periodic energy input especially at lower frequencies and duty cycles leads to gaps in the melt track resulting in discontinuous features. Pulsing was found to increase porosity as the entrapped gas bubbles formed in the melt pool do not have sufficient time to escape. The rapid solidification that follows each pulse traps these bubbles particularly when the solidification front advances too quickly resulting in porosity formation. 101 Chapter 7 – Conclusion This study investigated the feasibility of using beam pulsing in the laser powder bed fusion process of Ti-6Al-4V as a method to control melt pool geometry and reduce porosity, particularly in stable keyholing conditions. The study aimed to implement the positive effects of beam pulsing, a technique proven to reduce porosity and enhance melt pool flow in welding, to achieve similar results in SLM. Single melt tracks with varying frequencies (10Hz to 10kHz) and duty cycles (25%, 50% & 75%) as well as continuous tracks with four different powers (125W, 250W, 375W & 500W) were analysed cross-sectionally using Keyence and MicroCT. 1. When comparing the steady state portion of the continuous melt tracks, powder melt tracks were found to be narrower than their solid counterparts. For pulsed melt tracks, high-frequency pulsed tracks at lower duty cycles showed minimal differences in melt pool width when compared with the continuous melt tracks with the same average power. However, intermediate frequencies such as 100Hz and 500Hz at 25% and 50% duty cycles produced wider melt tracks. As the duty cycle increased to 75%, the variations in melt pool widths across all conditions diminished. 2. It was found that pulsing adversely affects the surface finish of both solid and powder melt tracks. As the frequency of pulsing increases, surface undulations become more pronounced, aligning with the laser-off periods. These undulations on the surface result in higher average Ra values indicating poor surface quality. 3. Cross-sectional imaging of the melt tracks revealed that keyholing occurred in most of the melt tracks. Some melt tracks displayed a V-shaped profile while higher power levels and duty cycles revealed a bimodal shape with a bowl at the top and a spike at the bottom. This shift in morphology is attributed to fluid flow, particularly the Marangoni effect which causes the melt pool to flow outward resulting in an increased melt pool width. 4. Pulsing was found to have a detrimental effect on porosity formation in the samples. It led to almost 33% more porosity compared to continuous melt tracks at the same average power. The porosity in continuous melt tracks did not exhibit any clear spatial pattern but pulsed melt tracks exhibited a more distinct and patterned porosity distribution. This is attributed to the laser turning off between pulses, which causes keyhole instability and leads to the entrapment of gas. 102 5. Lastly, the study investigated the potential of using simulations to predict melt pool geometry and understand porosity formation under various processing conditions for both continuous and pulsed laser melt tracks. The FAST model effectively predicted melt pool width but struggled with depth in keyholing scenarios due to its inability to capture fluid flow, recoil pressure and material vaporisation that occur during keyhole formation. However, integrating phenomenological models improved the predictions of melt pool geometry, though further refinement is still required. 6. Although exact quantification was not achieved, the CFD model effectively captured the trends observed in the experiments regarding melt pool geometry during keyholing as it accounted for fluid flow. The CFD model demonstrated that pulsing increased porosity as the gas bubbles trapped in the melt pool did not have enough time to escape. When the laser is turned off at the end of each pulse, due to rapid solidification and keyhole collapse, the gas bubbles do not get enough time to escape before the material solidifies leading to the formation of porosity. 7.1 Future scope Future work could explore shaping the laser pulse as a method to reduce porosity. A key area of focus in minimising porosity is controlling the solidification process. Ramping down the laser pulse can help by delaying solidification which provides more time for gas bubbles to rise and escape from the melt pool before the material solidifies. Previous studies [138,184] have shown this approach could be effective. The current process parameters result in a very high volumetric energy density (VED) leading to an unstable keyholing regime to melt a 200µm powder layer. At 500W, the melt depth reaches approximately 1.07mm which could remelt nearly five layers in a multilayered track. To address this, the experiments could be repeated with reduced power and increased scan speed to evaluate their effect on porosity and melt pool geometry. Literature [114] suggests that higher pulse overlap can reduce porosity by slowing the solidification rate. Another critical aspect is examining how pulsing can lead to the formation of site-specific microstructures. The cooling and solidification rates in the melt pool influence the final microstructure formation [187]. A slower cooling rate favours columnar grain growth where the grains are oriented along the direction of heat flow. A higher cooling rate leads to equiaxed grains which are more uniformly distributed. Shifting from columnar to equiaxed solidification refines the microstructure improving mechanical properties like strength and toughness. 103 References [1] W. 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Thesis/Dissertation
eng
Materials Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Sinclair, Chad W.
The effect of beam pulsing on laser beam processing of Ti-6Al-4V using a prototype laser additive manufacturing platform
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