Forestry, Faculty of
DSpace
UBCV
Li, Shiqing
Master of Science - MSc
University of British Columbia
Finger-jointed (FJ) lamellas are widely used in mass timber where bonding performance is critical for structural reliability. However, tool wear during FJ can affect wood surface characteristics, potentially affecting adhesive behavior and joint strength. In this study, Coastal Douglas-fir (DF), Hem-Fir (HF), and Western Redcedar (WRC) were machined using freshly sharpened and worn cutting tools and bonded with a polyurethane (PUR) adhesive to evaluate changes in surface topography, surface energy, adhesive wetting, ultimate tensile strength (UTS), and initial friction properties. The findings indicate that worn tool increased surface roughness (SA, SP, SV) for all wood species, which improved initial friction strength (IF-UTS) and coefficient of friction (IF-CoF), enhancing pre-cured joint integrity and stability. For DF, tool wear also increased UTS due to improved mechanical interlocking and adhesive penetration. In contrast, HF and WRC showed decreased UTS under worn tool conditions, despite increased roughness, IF-UTS, and IF-CoF, primarily due to reduced adhesive wettability and penetration. Glueline delamination remained low and was not significantly affected by wood species or tool wear, demonstrating the effectiveness of the PUR adhesive in accommodating surface variability. Overall, all FJ lamellas exceeded industry standards for UTS and failure modes, confirming reliable bonding performance. This study highlights the importance of balancing tool wear and wood species selection in FJ manufacturing. Moderate tool wear may be beneficial for DF, while maintaining sharp tools appears critical for HF and WRC. The findings provide valuable insights for optimizing machining strategies and improving the quality and consistency of structural FJ products in the wood industry.
INVESTIGATION OF THE EFFECT OF TOOL WEAR AND WOOD SPECIES ON SURFACE QUALITY AND PERFORMANCE OF FINGER-JOINT by Shiqing Li B.Sc., The University of British Columbia, 2023 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May 2025 © Shiqing Li, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Investigation of the Effect of Tool Wear and Wood Species on Surface Quality and Performance of Finger-Joint submitted by Shiqing Li in partial fulfilment of the requirements for the degree of Master of Science in Forestry Examining Committee: Julie Cool, Associate Professor, Forestry, UBC Supervisor Gregory Smith, Professor, Forestry, UBC Supervisory Committee Member William Nkeuwa, Application Engineer, Engineered Wood, Henkel Supervisory Committee Member Xiaoliang Jin, Associate Professor, Mechanical Engineering, UBC Additional Examiner iii Abstract Finger-jointed (FJ) lamellas are widely used in mass timber where bonding performance is critical for structural reliability. However, tool wear during FJ can affect wood surface characteristics, potentially affecting adhesive behavior and joint strength. In this study, Coastal Douglas-fir (DF), Hem-Fir (HF), and Western Redcedar (WRC) were machined using freshly sharpened and worn cutting tools and bonded with a polyurethane (PUR) adhesive to evaluate changes in surface topography, surface energy, adhesive wetting, ultimate tensile strength (UTS), and initial friction properties. The findings indicate that worn tool increased surface roughness (SA, SP, SV) for all wood species, which improved initial friction strength (IF-UTS) and coefficient of friction (IF-CoF), enhancing pre-cured joint integrity and stability. For DF, tool wear also increased UTS due to improved mechanical interlocking and adhesive penetration. In contrast, HF and WRC showed decreased UTS under worn tool conditions, despite increased roughness, IF-UTS, and IF-CoF, primarily due to reduced adhesive wettability and penetration. Glueline delamination remained low and was not significantly affected by wood species or tool wear, demonstrating the effectiveness of the PUR adhesive in accommodating surface variability. Overall, all FJ lamellas exceeded industry standards for UTS and failure modes, confirming reliable bonding performance. This study highlights the importance of balancing tool wear and wood species selection in FJ manufacturing. Moderate tool wear may be beneficial for DF, while maintaining sharp tools appears critical for HF and WRC. The findings provide valuable insights for optimizing machining strategies and improving the quality and consistency of structural FJ products in the wood industry. iv Lay Summary Finger-jointed (FJ) wood is commonly used in building materials like beams and panels for houses and tall wood buildings. This research studied how cutting tool wear affects the surface quality and bonding strength of FJ wood made from coastal Douglas-fir, Hem-Fir, and Western Redcedar, which are widely used in British Columbia. The study found that tool wear made the wood surface rougher, which helped hold the joints together before the glue cured. However, for some wood species, too much roughness reduced the final strength of the joint. The study also showed that Douglas-Fir performed well even with worn tools, while Hem-Fir and Western Redcedar needed sharp tools for the best results. These findings will help FJ manufacturers balance tool maintenance and production quality, leading to stronger, safer, and more reliable wood products for building construction. v Preface This thesis, entitled "Investigation of the Effect of Tool Wear and Wood Species on Surface Quality and Finger-Joint Performance," presents a comprehensive study led by Shiqing Li, under the supervision of Dr. Julie Cool. The research documented herein shows original methodologies and results, highlighting the independent work conducted by the author. The experimental design and methodology developed in this study are entirely novel and were designed collaboratively by Dr. Julie Cool and Shiqing Li. All experimental work, including preliminary tests, surface quality measurements, adhesive performance evaluations, and mechanical testing, was performed by Shiqing Li at the Centre for Advanced Wood Processing (CAWP), University of British Columbia. Initial training and supervision were provided by skilled technicians at CAWP, ensuring that all experimental procedures were rigorously followed and aligned with safety and quality standards. The preparation of finger-jointed (FJ) samples was conducted by Shiqing Li with the assistance of Matt Hermann at Urban Jacks, a Vancouver-based wood manufacturing company. Their collaborative efforts ensured that the FJ specimens were prepared under conditions reflective of industrial practice. The key findings from Sections 4.1, 4.2, 4.3 and 4.5 of this thesis were presented at the 26th International Wood Machining Seminar (IWMS-26) held on April 14–15, 2025, in Florence, Italy, allowing the research to be shared with leading scholars and professionals in the field of wood machining. Throughout my research and thesis writing, and with the approval of my supervisor, I used ChatGPT-4o to obtain suggestions for the original outline structure, clarify complex concepts, and enhance the overall organization of my arguments. While the GenAI tools served as a valuable vi supplementary resource, all final interpretations, conclusions, and analyses remain my own, ensuring that the work reflects my original thought process and academic integrity. This thesis contributes to the field of wood science and wood machining by providing new insights into the interactions between tool wear, wood species characteristics, and adhesive bonding performance in structural FJ products. The findings offer valuable recommendations for optimizing machining strategies and tool maintenance practices in industrial FJ production, ultimately supporting improved product quality and manufacturing efficiency in the wood products sector. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface ............................................................................................................................................ v Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................. xi List of Figures ............................................................................................................................. xiv Acknowledgements ................................................................................................................... xvii Dedication ................................................................................................................................. xviii Chapter 1: Introduction ............................................................................................................... 1 Chapter 2: Literature review ....................................................................................................... 6 2.1 Introduction ............................................................................................................................... 6 2.2 Wood characteristics ................................................................................................................. 7 2.2.1 Coastal Douglas-fir ........................................................................................................ 7 2.2.2 Hem-fir ........................................................................................................................... 8 2.2.3 Western Redcedar .......................................................................................................... 9 2.3 The machining process and the structural changes made to the materials ................................ 9 2.3.1 Facture of wood ............................................................................................................. 9 2.3.2 Microscopic analysis of machined surfaces ................................................................. 12 2.3.3 Quantification of machined surface characterization .................................................. 15 viii 2.3.4 Tool wear and its effect on machined wood ................................................................ 17 2.4 Adhesive chemistry and liquid interaction with machined wood surfaces ............................. 20 2.4.1 Overview of PUR adhesives ........................................................................................ 20 2.4.2 Wetting behavior of adhesives on wood surfaces ........................................................ 22 2.4.3 Surface free energy and adhesive interaction .............................................................. 26 2.4.4 Effect of tool conditions on surface energy ................................................................. 31 2.5 Characterization of bonding strength ...................................................................................... 31 2.5.1 Tensile strength of FJ wood ......................................................................................... 31 2.5.2 Delamination resistance of bonded joints .................................................................... 33 2.6 Effect of various factors on mechanical property of bonding ................................................. 34 2.6.1 Influence of tool wear .................................................................................................. 34 2.6.2 Impact of wood properties ........................................................................................... 35 2.7 Research gap ........................................................................................................................... 37 Chapter 3: Materials and methods ............................................................................................ 40 3.1 Testing materials ..................................................................................................................... 40 3.2 Finger jointing process ............................................................................................................ 41 3.3 Tool wear measurement .......................................................................................................... 44 3.4 Surface quality evaluation....................................................................................................... 45 3.4.1 Surface roughness ........................................................................................................ 45 3.4.2 Wettability and surface energy ..................................................................................... 47 3.5 Gluing performance tests ........................................................................................................ 49 ix 3.5.1 Ultimate tensile strength test ........................................................................................ 49 3.5.2 Cyclic delamination test ............................................................................................... 51 3.5.3 Microscopic observations ............................................................................................ 51 3.6 Initial friction measurements .................................................................................................. 52 3.7 Statistical analysis ................................................................................................................... 54 Chapter 4: Results and Discussions ........................................................................................... 55 4.1 Surface topography ................................................................................................................. 55 Effect of wood species .......................................................................................................... 55 Effect of tool wear ................................................................................................................ 61 Conclusion ............................................................................................................................ 65 4.2 Surface energy and PUR wetting rate ..................................................................................... 65 4.2.1 Surface energy ............................................................................................................. 67 4.2.2 Wetting rate .................................................................................................................. 74 4.2.3 Conclusion ................................................................................................................... 78 4.3 Initial friction (IF) ................................................................................................................... 78 4.3.1 Initial friction – ultimate tensile strength (IF-UTS) ..................................................... 79 4.3.2 Initial friction - coefficient of friction (IF-CoF) .......................................................... 81 4.3.3 Conclusion ................................................................................................................... 82 4.4 Gluing performances ............................................................................................................... 83 4.4.1 Ultimate tensile strength .............................................................................................. 84 4.4.2 Glueline delamination analysis .................................................................................... 92 4.4.3 Conclusion ................................................................................................................... 95 x 4.5 Implementation and recommendation for industry ................................................................. 96 Chapter 5: Summary and conclusions .................................................................................... 100 5.1 General conclusions .............................................................................................................. 100 5.2 Limitations ............................................................................................................................ 101 5.3 Recommendations for future research .................................................................................. 102 Bibliography .............................................................................................................................. 104 Appendices ................................................................................................................................. 152 Appendix A ................................................................................................................................. 152 Appendix B ................................................................................................................................. 156 B.1 Power consumption during FJ manufacturing with sharp tool. ................................... 156 B.2 Power consumption during FJ manufacturing with worn tool ..................................... 157 xi List of Tables Table 3.1: List summarizing the independent and dependent variables involved in the full factorial experimental design. ..................................................................................................................... 41 Table 3.2: Definition of each surface roughness parameters. ....................................................... 46 Table 3.3: Roughness parameters according to equation from ISO 25178. .................................. 46 Table 3.4: The surface tension components of the specific probe liquids (≈20 °C). .................... 49 Table 3.5: Failure Mode Criteria from ASTM D4688 (American Society for Testing and Materials, 2021). ............................................................................................................................................ 50 Table 4.1: Analysis of variance (ANOVA), including the F- and p-values for three surface roughness parameters characterizing FJ surfaces of three wood species prepared with freshly sharpened and worn cutting tools. A 95% confidence level is indicated by *, while ** was used to highlight a 99% confidence level. ................................................................................................. 55 Table 4.2: Mean surface roughness parameters (standard deviation of the mean in parenthesis) of specimens from three different wood species FJ with either freshly sharpened or worn cutting tools. Mean (n=20) within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. ......................................................................................................... 57 Table 4.3: Effect of wood species for SA and SV (tool conditions pooled; n=40). Means in a column followed by the same lowercase letter are not statistically different at the 95% confidence level........................................................................................................................................................ 57 Table 4.4: Effect of tool wear for SA and SV (wood species pooled; n=60). Means in a column followed by the same lowercase letter are not statistically different at the 95% confidence level........................................................................................................................................................ 62 xii Table 4.5: Analysis of variance (ANOVA), including the F- and p-values for dispersive component, acidic component, basic component, total surface energy and adhesive wetting rate of FJ surfaces of three wood species prepared with freshly sharpened and worn cutting tools. A 95% confidence level is indicated by *, while ** was used to highlight a 99% confidence level. ......................... 66 Table 4.6: Mean dispersive (𝛾𝑆𝐿𝑊), acidic (𝛾𝑆 +) and basic (𝛾𝑆 −) components and total surface energy (𝛾𝑆𝐿) (standard deviation in parenthesis) of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. ................................................................................................................... 67 Table 4.7: Mean wetting rate (standard deviation in parenthesis) of FJ lamellas made by freshly sharpened and worn cutting tools for all wood species. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. ............................... 74 Table 4.8: Mean wetting rate (standard deviation in parenthesis) of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with both freshly sharpened or worn cutting tools. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. .......................................................................................... 74 Table 4.9: Mean wetting rate (standard deviation in parenthesis) of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. .......................................................................................... 75 Table 4.10: Analysis of variance (ANOVA), including the F- and p-values for IF-UTS and IF-CoF of FJ lamellas of three wood species prepared with freshly sharpened and worn cutting tools. A xiii 95% confidence level is indicated by *, while ** was used to highlight a 99% confidence level........................................................................................................................................................ 79 Table 4.11: Mean initial friction ultimate tensile strength (IF-UTS) and mean initial friction coefficient of friction (IF-CoF) (standard deviation of the mean in parenthesis) of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. ..................................................................... 80 Table 4.12: Analysis of variance (ANOVA), including the F- and p-values for UTS and glueline delamination of FJ lamellas of three wood species prepared with freshly sharpened and worn cutting tools. A 95% confidence level is indicated by *, while ** was used to highlight a 99% confidence level. ........................................................................................................................... 84 Table 4.13: Mean ultimate tensile strength (UTS) and glueline delamination (standard deviation in parenthesis) of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. ............................... 84 Table 4.14: Mean glueline thickness of three FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. .............. 91 Table 4.15: Mean arithmetical mean deviation of the profile (SA), initial friction- ultimate tensile strength (IF-UTS), ultimate tensile strength (UTS) and delamination proportion of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. ................................................................................................... 98 xiv List of Figures Figure 3.1: Vertical finger jointed specimen of DF made of pieces that were defect-free over the first 50 mm from the glue line. ..................................................................................................... 42 Figure 3.2: Finger geometry with finger length (15mm), pitch (3.8mm), and tip (0.86mm) dimensions. ................................................................................................................................... 43 Figure 3.3: Adhesive was applied on half the fingers (from tip to mid-finger length) and the glue application (uniformity and quantity) was assessed by experienced operators prior to FJ with SPF (spruce-pine-fir) wood samples. ................................................................................................... 44 Figure 3.4: Tool wear measurements done for the FJ knives: cutting edge radius, edge recession (ER) and nose width (NW). .......................................................................................................... 45 Figure 3.5: Tool condition measurement for (a) freshly sharpened tool and (b) worn tool after FJ green WRC within its industrial context. ...................................................................................... 45 Figure 3.6: Example schematic of SA, SP and SV. ......................................................................... 46 Figure 3.7: Delamination of glue line after cycle of treatments. .................................................. 51 Figure 3.8: Schematic of (a) rider and incline surface and (b) coefficient of static friction tester........................................................................................................................................................ 53 Figure 4.1: Box plots of the mean surface roughness parameter (SA) for FJ specimens from three different wood species (Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC)) machined using sharp and worn cutting tools. The middle line represents the median, the "X" marks the mean, and the box spans the interquartile range (IQR). Whiskers extend to 1.5 times the IQR, and dots indicate outliers. ............................................................................................................................ 57 xv Figure 4.2: Transverse optical microscopy micrographs of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ surfaces machined with freshly sharpened and worn cutting tools........................................................................................................................................................ 58 Figure 4.3: Three-dimensional rendition the surface of a FJ specimen of Douglas-fir machined with sharp cutting tools. Magnification=200x. ............................................................................ 60 Figure 4.4: Transverse optical microscopy micrograph of a FJ specimen of Douglas-fir wood machined with sharp cutting tools. Magnification=250x. ............................................................ 60 Figure 4.5: Topography of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ surfaces machined with freshly sharpened and worn cutting tools. The images were acquired by the confocal microscope used to do the surface roughness measurements. ................................. 61 Figure 4.6: Box plots of the (a) maximum peak height (SP) and (b) maximum valley depth (SP) for FJ specimens from three different wood species (Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC)) machined using sharp and worn cutting tools. The middle line represents the median, the "X" marks the mean, and the box spans the interquartile range (IQR). Whiskers extend to 1.5 times the IQR, and dots indicate outliers. ........................................................................... 63 Figure 4.7: Percentages of cellulose, hemicellulose, and lignin in Douglas fir, Hem-fir and Western Redcedar (Lewis, 1950). ............................................................................................................... 72 Figure 4.8: Contact angle evolution with wetting time for (a) Douglas fir, (b) Hem-fir and (c) Western Redcedar when cutting with sharp tool and worn tool. Error bar shows the standard error of the mean. ................................................................................................................................... 77 Figure 4.9: Box plot showing the effect of wood species (Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC)) on the mean (a) IF-UTS, (b) IF-CoF under sharp tool and worn tool xvi conditions. The middle line represents the median, the "X" marks the mean, and the box spans the interquartile range (IQR). Whiskers extend to 1.5 times the IQR, and dots indicate outliers. ..... 80 Figure 4.10: Box plot showing the effect of wood species (Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC)) on the mean (a) UTS, (b) glueline delamination under sharp tool and worn tool conditions. The middle line represents the median, the "X" marks the mean, and the box spans the interquartile range (IQR). Whiskers extend to 1.5 times the IQR, and dots indicate outliers........................................................................................................................................... 85 Figure 4.11: Bar graph showing the frequency distribution of Failure Modes observed for each FJ lamellas tested in tension. All samples are pooled regardless of tool condition and wood species (n=120). ......................................................................................................................................... 86 Figure 4.12: Mean UTS of specimens with failure modes 4, 5, and 6 appear on the FJ lamellas made from Douglas-fir (DF), Hem-Fir (HF), and Western Redcedar (WRC) under sharp and worn tool conditions. The dashed line represents the mean UTS of FJ lamellas that failed according to Failure Modes 2 and 3. The dashed line represents the mean UTS of FJ lamellas that failed according to Failure Modes 2 and 3 .............................................................................................. 87 Figure 4.13: Representative cross-section image showing the adhesive penetration in Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ lamellas machined with freshly sharpened and worn cutting tools. The images were acquired using an optical microscope. ........................ 89 Figure 4.14: Proportion of failure modes appear on the FJ lamellas made from Douglas-fir (DF), Hem-Fir (HF), and Western Redcedar (WRC) under sharp and worn tool conditions. The percentages represent the proportion of each failure mode within cutting treatment. .................. 90 xvii Acknowledgements First and foremost, I would like to express my deepest gratitude to my supervisor, Dr. Julie Cool, for her continuous support, invaluable guidance, patience, thoughtfulness, and profound wisdom throughout my master’s study and research journey. Her encouragement and insightful advice have been instrumental in shaping this thesis and my academic growth. I would also like to sincerely thank my supervisory committee members, Dr. Greg Smith and Dr. William Nkeuwa, for their constructive feedback, thoughtful suggestions, and valuable insights on my research and experimental design. This project was conducted at the Centre for Advanced Wood Processing (CAWP) in the Faculty of Forestry at the University of British Columbia. I am very grateful to the CAWP staff (Brandon Chan, Claudia Ediger, and Lief Eriksen) for their technical support and assistance throughout my experiments. I would also like to extend my appreciation to Matt Hermann at UrbanJacks for his help and guidance during the manufacturing of the finger-jointed lamella samples. I would like to gratefully acknowledge Henkel Canada Corporation and Mitacs Accelerate for their financial support, which made this research possible. I am forever grateful to my parents and all my family members for their unconditional love, support, and belief in me throughout this journey. Finally, I would like to thank my beloved girlfriend, Sichen Zhou, for always being by my side, supporting me in life, and offering advice and encouragement through every step of my academic and personal growth. I would also like to thank my four cute and furry cats (Yutu, Qingcheng, Huisu, and Erer) for being with me through every moment, providing comfort, joy, and emotional support, and always managing to make me relax and soothe my anxiety during stressful times. xviii Dedication To my beloved parents, whose unconditional love, endless support, and encouragement have always been the strongest foundation in my life. To my wonderful girlfriend, Sichen Zhou, who has been by my side through every challenge and every step of my academic journey, offering constant support, understanding, and inspiration. And to my four lovely cats — Yutu, Qingcheng, Huisu, and Erer — whose companionship, cuteness, and comforting presence have brought me joy, calm, and peace of mind during the most stressful moments. This work is dedicated to all of you — for your love, patience, and unwavering belief in me. You are the most important part of my life, and I am forever grateful for having you with me on this journey. 1 Chapter 1: Introduction The building construction industry is a highly resource-intensive process and estimated to consume 40% of raw stone, gravel, concrete, and sand globally each year (Hussain & Kamal, 2015). It also accounts for 40-50% of the global output of greenhouse gas (GHG) emissions (Khasreen et al. 2009). Building materials made from wood use less energy and emit fewer GHGs than traditional energy-intensive building materials like steel or concrete (Natural Resources Canada, 2022). The carbon sequestering capabilities of trees and the subsequent use of this biomass in construction contribute positively to carbon dioxide reduction efforts (Gustavsson et al. 2006). The utilization of mass timber products, such as cross-laminated timber (CLT) and glue-laminated timber (glulam), in tall wood buildings is growing fast in North America. In British Columbia (BC), the Mass Timber Action Plan (Government of BC, 2022) aims to maximize opportunities for the mass timber construction sector, which should lead to the construction of 10 new mid-sized factories by 2035 (Government of BC, 2022). This should be associated with a significant increase in sales of mass timber products in both Canada and the USA (Sorensen, 2019). The need for manufactured long and high-performance structural finger-jointed (FJ) lamellas is therefore expected to increase. The anisotropic nature of wood makes it difficult to bond end-grain butt joints with sufficient strength to meet the in-service requirements (Vick, 1999). To compensate for the lower tensile strength of end-grain butt joints, a slope of 1 in 8 – for structural products – should be used to increase the wood-adhesive bonding area (Vick, 1999). The FJ is one of the most common end-grain joints used when manufacturing CLT and glulam. While FJ improves the tensile strength, when compared with that of butt joints, they only exhibit between 70% (Timbolmas et al. 2022) and 90% (Vick, 1999) of the tensile strength of clear wood. It is therefore essential to further 2 improve the bonding performance of FJ. Furthermore, the joints in FJ lamellas in CLT and glulam may be exposed to the weather, which influence their long-term bonding performance (Jokerst and Stewart, 1976; Cool and Hernández, 2011). Since the first publication on the application of structural FJ in Germany by Egner and Jagfeld (1966) (according to Jokerst (1981)), several studies on FJ made with PUR adhesives have been published. These experiments mainly focused on the effects of wood moisture content (MC) (Pommier & Elbez, 2006), end-pressure (Bustos et al. 2003a, 2011; Rao et al. 2014), curing time (Bustos et al. 2003a), cutting speed (Bustos et al. 2004; Hernández et al. 2011), and FJ profile configuration (Bustos et al. 2003b; Rao et al. 2012; Morin-Bernard et al. 2021). These studies were done on maritime pine (Pommier & Elbez, 2006), black spruce (Bustos et al. 2003a, 2003b, 2004, 2011; Hernández et al. 2011), poplar (Timbolmas et al. 2022), white ash, yellow birch, and white oak (Morin-Bernard et al. 2021) wood. Tool wear leads to surface quality defects and cell damage, which can affect the strength of FJ wood. For instance, studies have shown that increased tool wear leads to more cellular damage and a reduction in tensile and bending strengths of FJ silver fir (Vassileiou, 2011) and ponderosa pine (Reeb et al. 1998). Gradual wear of cutting edges not only reduces the quality of machining but also increases the risk of sudden catastrophic tool failure, which can lead to unplanned downtime (Wei et al. 2018). Itaya & Tsuchiya (2003) found that cutting edge wear directly contributes to an increase in power consumption of the machine tool, which has a serious impact on the cost of wood processing. Therefore, assessing and monitoring tool condition and changing tools regularly is essential to maintain consistent tool performance throughout the production process (Iskra & Hernández, 2012), which also reduces unplanned tool changes and minimizes downtime (Nasir & Cool, 2020). 3 In the Pacific Northwest Region, there is growing interest in using Douglas-Fir (Pseudotsuga menziesii) (DF), Hem-Fir (HF) is a random sawmill mix of western hemlock (Tsuga heterophylla) and amabilis fir (Abies amabilis) and Western Redcedar (Thuja plicata) (WRC) for structural FJ applications due to their geographical availability and cultural relevance for First Nations. However, the effect of tool wear on the surface quality and gluing performance of these wood species is not well-documented. DF, HF, and WRC differ significantly in density, chemical properties, and strength (Erickson & Harrison, 1974; Henze, 2006; Gil-Moreno et al. 2016), suggesting that they may not behave similarly in gluing processes (Mwambusi, 2016; Yusoh et al. 2021). These natural variations could lead to different responses to tool wear, impacting the quality and durability of glued joints. To our knowledge, there are limited studies investigating the impacts of cutting tool wear and PUR adhesive chemistry on the bonding performance of FJ for DF, HF, and WRC. Only a few studies focused on FJ using PUR adhesive with DF (Lehringer & Gabriel, 2014; Rammer et al. 2018; Yue et al. 2021), Eastern Hemlock (Sterley, 2004) and WRC (González-Prieto et al. 2021). While some studies have evaluated the effect of tool wear on surface quality and gluing performance for different wood species, very few have focused specifically on the FJ using these particular species. Based on tests conducted by the industry partner (Henkel Canada Corporation), PUR adhesives appear to behave as a lubricant when applied to the fingers of a FJ. This lubrication effect could significantly reduce the initial friction within the FJ, potentially compromising product integrity during the critical stages between glue application and high-speed movement of products on the assembly line. The impact of this lubrication effect may vary depending on the wood species, as different species exhibit distinct surface characteristics, such as density, porosity, and surface energy. For instance, DF, HF, and WRC differ significantly in their density and chemical 4 properties, which can influence how the adhesive interacts with the wood surface. Tool wear may worsen this issue by increasing wood surface roughness, which could further affect the interaction between the adhesive and the wood surface, thus influencing initial friction and overall product integrity. However, no scientific publications are currently available that specifically explore the relationship between tool wear, initial friction, wood species properties, and the performance of FJ made with PUR adhesives. As the demand for sustainable and high-performance building materials rises, optimizing the quality of FJ lumber becomes increasingly critical. One of the key challenges in meeting this demand is the effective management of tool wear and ensuring consistent gluing performance, both of which are essential for maintaining the structural integrity and durability of FJ products. Tool wear can negatively impact surface quality, leading to compromised bonding strength, while inconsistent adhesive performance can further undermine the reliability of the joints. The use of a laboratory FJ machining setting with relatively lower feed speeds and less end-pressure may not fully replicate real-world factory conditions. In actual FJ operations, higher feed speeds, combined with the natural variability in wood species, adhesive application, and end pressure, create more complex and dynamic conditions. Conducting the experiment in an industrial environment ensures that all parameters reflect these real-world conditions, leading to experimental results that are more realistic, credible, and suitable for direct technology transfer. This approach enables a thorough examination of the effects of tool wear and adhesive performance under practical constraints, offering valuable insights for optimizing FJ processes and improving tool maintenance strategies. Ultimately, the findings will support the industry in enhancing the quality and durability of FJ products, particularly for manufacturers working with the specified wood species. 5 The general objective of this study was to evaluate the impact tool wear levels on surface quality and bonding performance of FJ lamellas made of Coastal DF, HF, and WRC. Specifically, this study aimed to 1) assess the effect of tool wear on surface quality (surface roughness, wettability, and microscopic characteristics), 2) quantify the influence of surface quality (surface roughness, wettability, and microscopic characteristics) on the bonding strength of FJ lamellas using tensile and cyclic delamination tests, 3) correlate tool wear levels with bonding performance and determine the conditions that maximize bonding performance, and 4) quantify the initial friction for varying tool wear levels and different species. The main hypotheses to be investigated are: 1) tool wear is expected to increase surface roughness, which may negatively affect adhesive bonding by disrupting surface energy and wettability. 2) species differences (Douglas-fir, Hem-Fir, Western Redcedar) are expected to influence bonding performance due to variations in density, grain structure, and extractive content. 3) rougher surfaces may enhance initial mechanical interlocking, potentially improving pre-cure joint stability (IF-UTS and IF-CoF). 4) optimal bonding may require a balance between surface texture and adhesive compatibility, which could differ across species and tool wear levels. 6 Chapter 2: Literature review 2.1 Introduction The performance of FJ wood products is influenced by a combination of material properties, machining processes, adhesive interactions, and mechanical characteristics of bonded joints. This chapter systematically reviews these critical aspects, beginning with an examination of the materials and their characteristics, focusing on DF, HF, and WRC. Each wood species has distinct anatomical and mechanical properties that affect machining behavior and adhesive bonding performance. Following this, the review explores how machining processes impact wood structure (Section 2.3). It begins with an overview of wood fracture mechanisms (2.3.1) that occur during machining, including transwall, intrawall, and intercellular fractures, which influence surface roughness and adhesion. Then, microscopic analysis of machined surfaces topography (2.3.2) is discussed, highlighting how different machining methods alter surface morphology. Surface roughness measurements in wood research have predominantly focused on surfaces aligned with the primary grain orientations (longitudinal, radial, and tangential). The chapter then delves into the quantification of machined surface characterization (2.3.3), detailing methods such as profilometry and confocal microscopy used to assess surface roughness. Finally, the effect of tool wear on machined wood (2.3.4) is examined, with an emphasis on how increasing tool wear degrades surface quality and impacts bonding quality. The next section shifts focus to adhesive chemistry and liquid interactions with machined wood surfaces (Section 2.4), particularly PUR adhesives (2.4.1), which are widely used in structural applications due to their moisture resistance and durability. Wetting behavior of adhesives on 7 wood surfaces is then explored, discussing how surface energy and roughness affect adhesive spreading and penetration (2.4.3). This leads into surface free energy and adhesive interaction, outlining different methods for calculating surface energy and their relevance to wood bonding. Finally, the effect of tool conditions on wood surface characteristics (2.4.4) is examined, highlighting how machining parameters influence adhesion efficiency. The discussion then transitions to the characterization of bonding strength (Section 2.5), covering tensile strength of FJ wood (2.5.1) and delamination resistance of bonded joints (2.5.2) as key indicators of adhesive performance and durability. Section 2.6 reviews the effect of various factors on the mechanical properties of bonding, starting with the influence of tool wear (2.6.1) on bond strength, followed by the impact of wood properties (2.6.2) such as density and porosity on adhesive penetration and joint integrity. A comprehensive understanding of the factors that influence tensile strength and bond strength in FJ and other end-grain jointed wood products is essential for optimizing manufacturing processes and improving product quality. Finally, Section 2.7 identifies research gaps, addressing unexplored areas in tool wear and adhesive bonding (2.7.1) and challenges in surface quality measurement (2.7.2). 2.2 Wood characteristics 2.2.1 Coastal Douglas-fir DF has the broadest latitudinal range of any Western North American commercial conifer, stretching from central British Columbia along the Rocky Mountains into Mexico (Hermann & Lavender, 1990). The sapwood and heartwood of DF are distinctly colored, with sapwood being yellowish to reddish-white and heartwood ranging from yellowish-brown to dark red (Panshin & 8 de Zeeuw, 1980). DF exhibits a higher average density, which is comparable to other softwoods and makes it suitable for structural applications (Giagli et al. 2019). Furthermore, its density changes with age, increasing significantly in mature wood compared to juvenile wood (Bawcombe, 2012; Kimberley et al. 2017). Machining DF with a worn cutting tool likely removed earlywood cells due to their lower density than that of latewood (350 kg/m³ for earlywood vs. 800 kg/m³ for latewood) (Abdel-Gadir et al. 1993), and makes the former more susceptible to crushing and deformation during machining. In DF, the microfibril angle (MFA) decreases with age, from over 30° in juvenile wood to around 7° in mature wood (Erickson & Arima, 1974; Bawcombe, 2012). This reduction in MFA contributes to increased stiffness and strength in older trees, making them more suitable for structural uses. 2.2.2 Hem-fir HF is the combination of western hemlock (Tsuga heterophylla) and amabilis fir (Abies amabilis), and dominates the coastal forests of British Columbia (BCMFR, 2007). Traditionally used for lumber and other low-value products, second-growth HF has potential in high-value engineered wood products like CLT and laminated veneer lumber (LVL) (Wang et al. 2010a, b; Wang & Dai, 2013). Although HF is challenging to dry uniformly due the existence of wet pockets and large initial MC variation in amabilis fir (Shahverdi, 2015), it can be converted into higher value building products due to its uniform grain structure and machinability (Wang et al. 2018). 9 2.2.3 Western Redcedar WRC is an old-growth species from the Pacific Northwest, now found from northern California to southeast Alaska and interior temperate rainforests (Minore, 1990). WRC is straight-grained, resin-free, and has moderate specific gravity. While prone to splitting, it bonds well with a range of adhesives and is durable due to a high extractive content in its heartwood (Swan et al. 1988). These extractives degrade cutting tools quickly due to oxidation (Darmawan et al. 2011). Its stability, durability, and insulating properties make it valuable for specialized uses such as shingles, fences, balconies, patios and boats (Bothwell, 1998). 2.3 The machining process and the structural changes made to the materials 2.3.1 Facture of wood Wood, as a natural, anisotropic, and hierarchical material, exhibits complex fracture behavior influenced by its structure, environmental conditions, and external loading. Wood's anisotropic nature significantly affects its fracture behavior. The grain orientation, density, and cellular structure play pivotal roles in determining fracture toughness and failure modes. For instance, fracture toughness is generally higher in the tangential-longitudinal direction compared to the radial-tangential direction (Conrad et al. 2002; Tukiainen, 2015). This anisotropy arises from the alignment of wood cells and the resulting stress distribution during loading. The cellular structure of wood, including tracheids, rays and the middle lamella, influences crack propagation. Under longitudinal tensile loads, cracks often initiate in the S₂ layer of tracheids and propagate along the S₁/S₂ interface (Wang et al. 2020). In contrast, under torsional loading, cracks may propagate along the tangential or radial directions depending on the grain orientation (Chen et al. 2006). 10 Fracture in wood can occur in different modes, including Mode I, Mode II and Mode III (Figure 2.1). Mode I corresponds to the opening or tensile mode, where the crack surfaces move directly apart. Mode II is the sliding or in-plane shear mode, where the crack surfaces slide across one another in the direction perpendicular to the leading edge of the crack. Finally, Mode III is the tearing or antiplane shear mode, where the crack surfaces move parallel to the leading edge of the crack (Conrad et al. 2002; Saoud et al. 2019). Mixed-mode fractures are common in real-world scenarios, where both tensile and shear stresses act on the material. The interaction of these stresses leads to complex crack propagation paths, often influenced by the material's microstructure and environmental factors (Tukiainen, 2015; Kuo et al. 2016). Figure 2.1: Schematic of Mode I, Mode II and Mode III fracture (Chen et al. 2006). MC and temperature significantly affect the fracture behavior of wood. Increased MC generally reduces the stiffness and strength of wood, making it more prone to fracture (Larsen & Ormarsson, 2013; Tukiainen, 2015). These cracks often initiate at the interface between ray tissues and 11 tracheids due to differential shrinkage (Gao et al. 2022). However, elevated temperatures can induce brittleness, especially in thermally modified wood, leading to higher fracture susceptibility (Tukiainen, 2015). The drying process also induces stresses and strains in wood, leading to the formation of microcracks. The propagation of these cracks is influenced by the moisture gradient and anisotropic shrinkage, with higher shrinkage ratios in the tangential direction compared to the radial direction (Larsen & Ormarsson, 2013; Gao et al. 2022). The relationship between tool wear and wood fracture is complex. Tool wear alters the cutting tool's geometry, which in turn affects the stress distribution during the machining process. This altered stress distribution can lead to increased stress concentrations in the wood, making it more susceptible to fracture. As tools wear, the cutting forces required to machine the wood increase. This increase in force can lead to higher mechanical stresses in the wood, particularly in the vicinity of the cutting edge. Elevated stresses can initiate cracks in the wood, which may propagate and result in fracture (Orłowski et al. 2013). Tool wear results in an increase in the cutting edge radius (Figure 2.2). These changes can alter the cutting tool's interaction with the wood, leading to a less efficient cutting action. This inefficiency can cause the wood to undergo plastic deformation rather than clean cutting (Bendikiene et al. 2015; Torkghashghaei et al. 2023). 12 Figure 2.2: Schematic showing the cutting edge radius of new tool and worn tool. Tool wear can cause increased friction and cutting forces, thus generating higher temperatures at the cutting interface. These elevated temperatures can cause thermal damage to the wood, leading to a reduction in its mechanical properties and an increased susceptibility to fracture (Krilek et al. 2023; Muhammed et al. 2023). Tool wear also influences the surface roughness of the machined wood and the nature of chip formation. A worn tool tends to produce rougher surfaces, because the greater friction can cause cell damage and deformation in the wood. The formation of discontinuous chips is often associated with higher energy expenditure and increased stress concentrations in the wood, which can lead to fracture (Keturakis et al. 2017; Guo et al. 2021). 2.3.2 Microscopic analysis of machined surfaces Wood is known for its complex anisotropic nature, structured through multiple hierarchical levels. These structural characteristics, from the macroscopic to the sub-microscopic scale, significantly 13 influence wood properties. On a cellular level, factors such as tracheid length, diameter, cell wall thickness, shape, cell type composition, and porosity impact the wood’s overall macroscopic traits. Traditional wood anatomy imaging employs transmission light microscopy of thin sections to observe cellular structures, while electron microscopy offers higher magnifications. Scanning electron microscopy (SEM), for instance, provides a detailed view of the three-dimensional (3D) structure of wood tissue (Trtik et al. 2007). Processing and sawing wood can alter the surface quality, which is reflected in microstructural changes like cell damage. Reeb et al. (1998) observed that strength loss in FJ ponderosa pine increased with tool wear as it increased the compressive force and depth of crushed cells. Increased tool wear generally leads to more cellular damage, reducing shear gluing strength as the cells in the damaged region can be weaker, thus increasing the potential for bond failure (Bustos et al. 2010). Many researchers have used SEM or optical microscopy to examine wood cell damage from different machining parameters, such as laser cutting (Aniszewska et al. 2020) and face milling and oblique cutting (Cool & Hernández, 2009; de Moura et al. 2010). Figure 2.3 illustrates the cellular damage from wood machining process. 14 Figure 2.3 End-grain SEM micrographs of cellular damage in a face-milled eastern white pine surface (de Moura et al. 2010). In an advanced wood composite product, the adhesive is required to fill the gap between two wood adherends. The degree of penetration of the adhesive into the wood tissue reportedly has a major influence on the bond formation and the achieved bond strength. The penetration depth of an adhesive can be monitored by a range of methods, e.g., the light microscopy of cross sections (Sernek et al. 1999), scanning thermal microscopy (Konnerth et al. 2008) and SEM (Hass et al. 2012). Figure 2.4 shows the glue penetration of a bond line for planed adherends. 15 Figure 2.4 Transverse environmental scanning electron microscopy micrographs of glued black spruce wood surfaces that were conventional planed (Cool & Hernández, 2011). 2.3.3 Quantification of machined surface characterization Contact stylus instruments measure surface texture based on two-dimensional (2D) profilometry (Whitehouse, 2023). Marian et al. (1958) found the stylus sensitive enough to record irregularities due to processing and wood anatomy. A Talysurf stylus with a 90° pyramid and 2 μm radius spherical tip, as described in ISO 12179:2021 (International Organization for Standardization, 2021), is unlikely to damage wood. This setup has been widely used for wood surface assessment (Sadoh & Nakato, 1987; de Moura & Hernández, 2006; Kilic et al. 2006; Hendarto et al. 2006; Javorek et al. 2015; Magoss, 2015). However, contact stylus methods are inherently limited by their single-line tracing approach, which may miss surface texture changes that occur over larger areas. This limitation is important in the case of wood surfaces, which often have complex textures due to their anatomical characteristics. Specifically, the presence of tiny defects and grain patterns 16 can interfere with the stylus’s ability to accurately capture surface roughness, leading to potentially misleading data (Gurau & Irle, 2017). Moreover, a stylus can overlook features smaller than stylus radius (Sinn et al. 2009) as shown in Figure 2.5. Figure 2.5: Sketch shows the stylus tip cannot trace the shape properly if the width of the groove (scratch, etc.) is narrower than the radius of the stylus tip (Whitehead et al. 1999). Laser techniques involve 3D measuring instruments based on laser light reflection from the surface, which is recommended for wood surfaces (Lemaster & Beall, 1996; Hu & Afzal, 2005; Goli & Sandak, 2016). The laser technique is recommended to measure average surface profile irregularities (Gurau et al. 2001; Sandak & Tanaka, 2003) and showed high accuracy for measuring wood surface roughness (Lundberg & Porankiewicz, 1995; Westkämper & Schadoffsky, 1995; Sandak & Tanaka, 2003; Sinn & Sandak, 2009). Profile measurements can quickly identify and separate processing roughness from anatomic roughness but may miss some topographical details, necessitating multiple scans. Area measurements, providing 3D surface maps, cover more topographical variations and allow for an easier visualization of surface defects. They are particularly useful for wood, which has different properties in three grain directions (Gurau & Irle, 2017). Laser confocal microscopy allows for the 17 capture of fine details in the surface profile of wood, offering higher resolution compared to traditional stylus methods. This is due to its ability to reconstruct surfaces in both 2D and 3D, providing comprehensive data for analysis (Magoss et al. 2022; Bezak et al. 2016). Laser confocal microscopy also could generate 3D models of the wood surface, allowing for a more detailed analysis of surface roughness, which is critical for applications where surface texture affects material performance, such as in timber pile interfaces (Bezak et al. 2014; Rauthause et al. 2020). Consequently, laser confocal microscopy was employed to measure the surface roughness of FJ pieces in area measurements in this study. 2.3.4 Tool wear and its effect on machined wood Tool wear gradually develops during regular use, leading to significant changes in tool geometry (Porankiewicz et al. 2005). As highlighted by Klamecki (1979), abrasive wear is one of the main factors causing the recession of tool edges, which is the degradation or wear of the cutting edges of tools. Generally, this wear includes the recession of the cutting edge and the formation of a wear land, which alters cutting angles and raises temperatures due to increased friction (Nasir & Cool, 2021). Generally, edge recession at various linear cutting distances is a common way to measure and quantify tool wear (Porankiewicz et al. 2005; Bendikiene et al. 2015; Fahrussiam et al. 2016; Szwajka & Trzepieciński 2016). Additionally, the width of the cutting edge nose (Guo et al. 2014) and radius (Ismail et al. 2020) are also used to evaluate and quantify tool wear. The schematic of how edge recession, cutting edge nose width and cutting edge radius are measured on the cutting tool tip is shown in Figure 2.6. 18 Figure 2.6: Illustration of edge recession and nose width measurement on a cutting tip (Guo et al. 2014). The influence of tool wear on the quality of wood machining has been a focal point of numerous research studies, presenting a comprehensive exploration into how tool wear affects various characteristics and performance metrics of wood products. Increased tool wear would result in higher damage levels to the wood surface and subsurface like crushed and collapse wood cells (Figure 2.7) and a reduction in adhesive penetration, which decreases the mechanical interlocking between wood and adhesive (Hernández & Rojas 2002). For example, tensile shear strength tends to be negatively impacted (Bustos et al. 2010) by tool wear. Moreover, it is known that increased tool wear results in higher surface roughness of machined surfaces (Guo et al. 2014; Djurković et al. 2019). 19 Figure 2.7 Micrographs of cut surface of radiata pine cut with (a) sharp tool and (b) worn tool (Bustos et al. 2010). Magnification: 10x. Irregularities left on wood surfaces by cutting tools are influenced by the wood-tool interactions. Cutting conditions significantly influence cutting energy and forces (Cakmak, 2022; Zhou et al. 2024), and consequently the wood surface quality. Increasing cutting speed or rotation speed (i.e., RPM) can enhance wood surface quality (McKenzie, 1961; Lemaster & Beal, 1993; Mitchell & Lemaster, 2002). Mitchell & Lemaster (2002) explored how machine parameters, including feed speed, rotation speed, tool orientation and tool wear, affect surface quality. During machining processes such as sawing, planing and milling in solid wood and engineered wood products, tool wear generally led to greater wood surface roughness, but effects varied with material type and 20 machining conditions (Saloni et al. 2011; Ābele et al. 2013; Guo et al. 2014; Kminiak et al. 2015; Bendikiene & Keturakis, 2016; Keturakis et al. 2017; Djurković et al. 2019). 2.4 Adhesive chemistry and liquid interaction with machined wood surfaces 2.4.1 Overview of PUR adhesives PUR is a polymer type extensively used in numerous applications due to its desirable characteristics, such as low density, low thermal conductivity, high strength-to-weight ratio, and moisture resistance (Luo et al. 2020). PUR adhesives primarily consist of one- and two-component formulations. One-component adhesives, also referred to as moisture-cure PURs, cure in the presence of moisture and form a highly crosslinked network due to their NCO-terminated structure (Šebenik & Krajnc, 2007). Rath et al. (2008) highlighted several benefits of one-component PURs, including their ease of production and application as a single package. Additionally, since water serves as the reactant, one-component PUR formulations exhibit superior adhesion, abrasion resistance, thermal stability, hardness, and resistance to chemicals and solvents, and contain fewer volatile organic compounds (VOCs) compared to two-component systems. PUR adhesives are widely used in various applications, particularly in engineered wood products, due to their bonding strength, curing time, and moisture resistance (Luo et al. 2020) as well as ductility (Lehringer & Gabriel, 2014). The curing process of PUR involves four stages: 1) moisture penetrates PUR and reacts with the NCO-terminated prepolymer, forming carbamic acid; 2) Carbamic acid, being unstable, breaks down into a primary amine and CO2; 3) The highly reactive primary amine combines with NCO to produce a urea linkage; 4) Urea groups can continue to react with excess NCO, generating biuret and polyurea structures (Rath et al. 2008). Specific schematic as shown in Figure 2.8 (Ren, 2010). Consequently, the cured adhesive consists of both urethane and urea segments, known as "poly(urethane urea).” Indeed, for the application of PUR 21 in wood products, free isocyanate can react with hydroxyls present in wood biopolymers to form urethane linkages (Britain & Gemeinhardt, 1960) (Figure 2.9). Urethane groups can also further react with excess NCO to create allophanates (Figure 2.9). The curing process can take from several hours to days, contingent on the specific adhesive and environmental conditions (Vick & Okkonen, 1998). Figure 2.8 Curing chemistry of PURs. Figure 2.9 Formation of urethane and allophanate linkages. 22 Mechanical interlocking is another critical mechanism that contributes to the adhesion of PUR adhesives on wood surfaces. This mechanism involves the physical penetration of the adhesive into the porous structure of the wood substrate, creating a strong mechanical bond as the adhesive cures and hardens (Vick & Kuster, 1992). The depth of penetration of the adhesive into the wood substrate is influenced by several factors, including the viscosity of the adhesive, the surface roughness of the wood, and the pressure applied during the bonding process. Adhesives with lower viscosity can penetrate deeper into the wood structure, resulting in a stronger mechanical interlock (Wang et al. 2017; Rusakov et al. 2022). A joint mechanical bond is often defined as the connection between two or more components achieved through mechanical means, often involving interlocking, fastening, or the application of forces to maintain the integrity and position of the joint (Shang et al. 2019). 2.4.2 Wetting behavior of adhesives on wood surfaces Wetting occurs when a liquid contacts a solid surface (Shi & Gardner, 2001). The wettability of the surface is often quantified by the contact angle method. This requires measuring the angle formed between the solid surface and a tangent line drawn through the point where the atmosphere, liquid, and solid meet (Zisman, 1963). For instance, Figure 2.10 illustrates various degrees of wettability on a smooth solid surface. Figure 2.10 Schematic representation of the different measurement of contact angles (θ) (Yuan & Lee, 2013). 23 Assuming the liquid is water, a contact angle significantly less than 90° indicates a hydrophilic surface or high wettability so the liquid spreads easily across the surface. Conversely, a contact angle significantly greater than 90° indicates the surface is hydrophobic, and the droplet retains its round, compact shape to minimize contact with the surface (Yuan & Lee, 2013). Surfaces with a contact angle exceeding 150° are referred to as 'super hydrophobic' (Law, 2014). To achieve strong adhesive joints, it's crucial to have good adhesive wetting (Zisman, 1963; Baier et al. 1968). Adhesive wettability, which refers to the adhesive's ability to intimately contact a surface, was extensively studied in the 1960s and 1970s, focusing on wood (Gray, 1962; Collett, 1972; Hse, 1972). These studies often measured instantaneous, or equilibrium contact angles at the solid-adhesive interface. Historically, studies focused on the thermodynamics of the liquid-solid interaction of wood, using instantaneous or equilibrium contact angles (Nguyen & Johns, 1978), often overlooking the importance of liquid spreading and penetration. However, these processes are crucial for evaluating wood adhesion, as they contribute to mechanical interlocking, a key aspect in adhesion theory. Therefore, understanding the entire wetting process, including contact angle formation, spreading, and penetration, offers more meaningful insights into adhesive wettability than just comparing initial or equilibrium contact angles (Shi & Gardner, 2001). Recent research has emphasized the importance of measuring contact angle changes over time in liquid wetting studies (Maldas & Kamdem, 1998; Scheikl & Dunky, 1998). Berg (1993) describes wetting as the molecular interactions occurring at the interface between liquids and solids, manifested in three main ways: contact angle formation, spreading, and penetration of the adhesive into the solid substrate (Shi & Gardner, 2001). Penetration, the third manifestation, largely depends on the solid's surface structure, with no liquid penetration occurring 24 in nonporous materials. Both spreading and penetration alter the contact angle over time (Shi & Gardner, 2001). Since wood is a lignocellulosic and porous material, liquid penetration into its pores is a critical factor in bond formation. The adhesive wetting on wood surfaces includes all three of these above manifestations (Shi & Gardner, 2001). The specific wood species being bonded is crucial, even though some behaviors are similar across different species (Singh & Rana, 2013). The key characteristics of wood that influence bonding include its porosity, anisotropy, dimensional instability, and surface properties. Wood’s cellular structure means its porosity varies by species, and its anisotropic nature causes different physical and mechanical properties depending on the orientation of the wood elements. Moisture changes cause wood to expand and contract, a hygroscopic behavior leading to dimensional instability. Chemical heterogeneity, surface deactivation, weak boundary layers, and processing properties like machining, drying, and aging also affect wood's surface properties (Nichols et al. 1985; Gipson et al. 1992). Extractives play a dominant role in wood surface characteristics, making it challenging to summarize these properties due to the complexity of wood structure and the numerous wood species. Extractives in wood, such as those found in WRC (thujaplicins, lignans, thujic acid, methyl thujate, and various guaiacyl-based compounds) contribute to its hydrophobic nature, increasing the contact angle and reducing water absorption. This is evident in studies where wood samples that have had their extractives removed show decreased contact angles, indicating increased wettability due to the removal of those hydrophobic extractives (Nzokou & Kamdem, 2004; Barkai et al. 2017). The chemical composition of extractives plays a significant role in wettability. For instance, abietic acid found in most softwoods significantly increases water absorption time and contact angle, suggesting that specific extractives can drastically alter 25 wettability (Östenson et al. 2003). The presence of extractives can impact on the adhesion properties of wood as different properties of extractive vary the wetting on the wood. For example, polar and hydrophilic extractives (tannins and certain phenolic compounds) are attracted to water and can increase the surface's affinity for water, while nonpolar extractives, like waxes and fats, are repelled by water and can create a hydrophobic surface (Nzokou & Kamdem, 2004; Papp et al. 2020). High extractive content can lead to poor glue adhesion, as seen in pine heartwood, where increased extractive content correlated with more failure happening in the glueline during water boiling tests (Nussbaum & Sterley, 2001). Differences in components and structure between sap- and heartwood also impact adhesive wettability (Shi & Gardner, 2001). The complexity of wood surface properties means it’s important to understand the specific wood species used in bonding, as each may present unique challenges and behaviors. Scholars have noted that increased surface roughness enhances liquid diffusion, leading to better wetting properties. The wetting behavior on rough surfaces can be described by the Wenzel and Cassie-Baxter models, which states that increased roughness enhances wettability for hydrophilic materials, leading to superhydrophilicity (Wenzel, 1936). Conversely, for hydrophobic materials, roughness can increase resistance to wetting, resulting in larger contact angles (Law & Zhao, 2015). Aguilera et al. (2016) found that machining with worn tools generated wood surfaces characterized by smaller contact angles, thereby improving wood wetting properties. River et al. (1991) explained that wood species with different cell structures exhibit varying roughness levels; earlywood, with its large lumens and thin cell walls, forms deep troughs and results in a rougher surface, whereas latewood, characterized by smaller lumens and thicker cell walls, produces a smoother surface. 26 The way wood fractures during cutting also influences roughness. Transwall fractures, which pass through the cell wall and lumen, expose lumens and cleaved walls, leading to increased roughness, especially in earlywood. In contrast, intrawall fractures remain within the cell wall, creating a smoother surface commonly found in latewood. Under certain conditions, such as high moisture or temperature, intercellular fractures occur along the middle lamella, further affecting surface texture. Additionally, the sharpness of the cutting tool significantly impacts roughness; sharp tools maximize roughness by promoting transwall fractures, while dull or improperly sharpened tools compress and smear cell walls, resulting in a smoother and less porous surface. Rough surfaces may cause over-penetration of adhesives by increasing the true surface area and thus the proportion of glue decreases in relation to that surface area (Follrich et al. 2010). Furthermore, de Moura & Hernández (2005) observed that higher surface roughness correlates with better wetting properties, facilitated by intrawall cracks. Liquids can penetrate these cracks and crevices, adhering to the surface (River et al. 1991). Borgin (1971) states that this type of fracture exposes the S-1/S-2 region, which is rich in hydroxyl units from cellulose and hemicellulose, making it highly polar and conducive to wetting. River et al. (1991) also note that as tool wear increases, the cell walls compress, resulting in a smoother and less porous surface compared to one made with a sharp tool. Consequently, roughness decreases while the contact angle increases, indicating reduced surface wettability. 2.4.3 Surface free energy and adhesive interaction The surface free energy of wood, much like its wettability, is a valuable parameter often linked to the biological interactions of wood. This energy can be determined through various methods that analyze the contact angle of liquids on the wood surface (Gindl et al. 2001a). Both recent and past 27 studies have explored how differences in wood surface free energy relate to properties like porosity and anisotropy. Research has shown that factors such as wood species, surface roughness, pH value, and aging time all affect surface free energy (Cao et al. 2005; Gindl et al. 2001b; Little et al. 2013; Mohammed-Ziegler et al. 2004). The most practical method to determine surface energies is through the measurement of contact angles on solid surfaces (Wu & Nancollas, 1999). The foundational theory for contact angles of pure liquids on solids traces back almost two centuries, based on the Young’s equation (equation 2.1). This section discusses five methods for determining the surface energy (𝛾𝑆) and interfacial energy (𝛾𝑆𝐿), with a notable advance being the introduction of a combining rule. This rule, which eliminates 𝛾𝑆𝐿 from the Young’s equation, leverages molecular considerations and theories of intermolecular forces, as noted by Good et al. (1958, 1960). While detailed theoretical descriptions are available, this summary focuses on the key equations. 𝛾𝐿𝑐𝑜𝑠𝜃 = 𝛾𝑆 − 𝛾𝑆𝐿 (2.1) where 𝛾𝐿 is the determined surface energy or surface tension of the test liquid, 𝜃 is the contact angle of test liquid on the solid, 𝛾𝑆 is the surface energy of the solid and 𝛾𝑆𝐿 is the surface energy of liquid/solid interfacial energy. 2.4.3.1 Zisman approach A significant development in contact angle research was made by Zisman (1963), who measured contact angles for various homologous liquid series on different low-energy solids. In Zisman's method, the cosine of the contact angles (θ) of these liquids is plotted against the surface tension (𝛾𝐿) of the liquids, following Equation 2.2 (Figure 2.11). Zisman introduced the concept of critical surface energy (γc), which is defined as the surface tension at which a reference liquid completely 28 wets the surface (𝛾𝐿 = 𝛾𝐶). By extrapolating a straight line formed by the experimental values of reference liquids to 𝑐𝑜𝑠𝜃 = 1 (𝜃 = 0), the complete wettability of the surface can be determined. 𝑐𝑜𝑠𝜃 = 1 − 𝑏(𝛾𝐿 − 𝛾𝑆) (2.2) where b is the regression line of the plot in Figure 2.11, and the surface tension of liquid could be measured by using Wilhelmy Plate Method (Luo et al. 2015) and the Pendant Drop Method (Berry et al. 2015; Paap et al. 2022). Figure 2.11 Zisman plot of contact angles (θ) against the surface tension (γL). 2.4.3.2 Geometric and harmonic mean approach, or two liquid methods Proposed by Wu (1971), this approach is widely accepted for calculating the intermolecular energy between two materials, which comprises a dispersion component and a polar component. Since surface free energy (or surface tension) correlates with intermolecular energy, it can be represented as a sum of these two components: dispersion (𝛾𝐷)and polar (𝛾𝑃). The interfacial energy (𝛾𝑆𝐿) between a liquid and a solid polymer can be evaluated using either the harmonic mean equation 29 (Wu, 1971) or the geometric mean equation (Owens & Wendt, 1969), represented in Equation 2.3 and Equation 2.4, respectively. 𝛾𝑆𝐿 = 𝛾𝑆 + 𝛾𝐿 − 4𝛾𝑆𝐷𝛾𝐿𝐷𝛾𝑆𝐷 + 𝛾𝐿𝐷 − 4𝛾𝑆𝑃𝛾𝐿𝑃𝛾𝑆𝑃 + 𝛾𝐿𝑃 (2.3) 𝛾𝑆𝐿 = 𝛾𝑆 + 𝛾𝐿 − 2𝛾𝑆𝐷𝛾𝐿𝐷 + 𝛾𝑆𝑃𝛾𝐿𝑃 (2.4) Research has shown that the surface energy values obtained from solid polymers can vary depending on the liquids used for measuring the contact angles. This variation is observed when using the harmonic mean, geometric mean, and equation of state approaches. For accurate determination of surface free energy values through geometric and harmonic mean equations, it is crucial to use pairs of polar and non-polar liquids (Shimizu & Demarquette, 2000). This approach ensures that the calculated surface energy values are reliable and consistent across different materials. 2.4.3.3 Acid–base theory, or three liquid methods Developed by Fowkes & Zisman (1964), Fowkes (1987), van Oss et al. (1987, 1988) and Chaudhury (1996), the acid-base theory is considered the most complex. This theory posits that surface free energy comprises a Lifshitz–van der Waals component 𝛾𝑖𝐿𝑊 which replaced the disperse component 𝛾𝑖𝐷 in the Geometric and Harmonic Mean Approach and Lewis acid–base component 𝛾𝑖𝐴𝐵 which replaced the polar component 𝛾𝑖𝑃, the latter of which can be further divided as per Equation 2.5. 𝛾𝑖𝐴𝐵 = 2𝛾𝑖+𝛾𝑖− (2.5) The interfacial tension between the solid and liquid interface can then be defined by Equation 2.6: 𝛾𝑆𝐿 = 𝛾𝑆 + 𝛾𝐿 − 2√𝛾𝑆𝐿𝑊𝛾𝐿𝐿𝑊 − 2√𝛾𝑆+𝛾𝐿− − 2√𝛾𝑆−𝛾𝐿+ (2.6) Combining Young’s equation (Equation 2.1) and Equation 2.6 to Equation 2.7: 30 𝛾𝐿(1 + 𝑐𝑜𝑠𝜃) = 2√𝛾𝑆𝐿𝑊𝛾𝐿𝐿𝑊 + 2√𝛾𝑆+𝛾𝐿− + 2√𝛾𝑆−𝛾𝐿+ (2.7) To determine the components of surface free energy (𝛾𝑆𝐿𝑊) and parameters 𝛾𝑆+ and 𝛾𝑆− of a solid, contact angles of at least three liquids with known surface tension components (γLLW, γL+ , and γL−) must be measured, with at least two of these liquids being polar (Gindl et al. 2001a). 2.4.3.4 Equation of state or one liquid method An equation of state for solid and liquid interfacial tension (Equation 2.8) developed by Neumann et al. (1974) γSL = γL + γS − 2γLγSe−β(γL−γS)2 (2.8) where β is the average value 0.0001057 [(m2 mJ−1)2] of empirical constant. This equation of state can be combined with Young’s equation and allow to use the contact angle of single test liquid (polar or disperse) with known γL to determine the γS. 2.4.3.5 Selected approach for the study The acid-base approach (vOCG) was selected for evaluating the surface free energy of the wood fingers cut by sharp and worn knives in this study. The acid-base approach is particularly valuable because it provides detailed information about surface chemistry, including values for both acid and base components, as well as polar and dispersive components of surface free energy. This level of detail is especially useful when working with chemically heterogeneous materials like wood (Gindl et al. 2001). Understanding more than two components of surface free energy is crucial for characterizing wood surfaces, particularly when addressing issues related to coating and adhesion (Gardner, 1996; Shen et al. 1998; De Meijer & Militz, 2000; Gindl et al. 2001). 31 Gardner (1996) and Wålinder & Gardner (2002) highlight that the vOCG method offers greater accuracy and more comprehensive information compared to the Owens-Wendt-Rabel-Kaelble method. Therefore, a preference for the acid-base approach is recommended, as it provides values closest to the means of all methods, ensuring more reproducible results (Gindl et al. 2001). 2.4.4 Effect of tool conditions on surface energy Tool wear significantly impacts the surface energy of softwood, the wetting ability of adhesives like PUR, and the wood-adhesive interaction. As tools wear, surface irregularities (torn grain, fuzzy grain and raised grain) appear, which can alter the surface energy and affect how adhesives interact with the wood surface. Tool wear leads to increased surface roughness, which can negatively impact the surface energy of wood. A rougher surface generally has higher surface energy, which can improve adhesive wetting but may also lead to uneven adhesive distribution (Keturakis et al. 2017; Talić & Hodžić, 2024). The wetting ability of PUR adhesives is influenced by the surface roughness and surface energy of the wood (Holeček et al. 2023). Higher surface energy can enhance the wetting ability of adhesives, leading to better bonding performance (Jiang et al. 2013; Leggate et al. 2020). Surface preparation methods, such as sanding or milling, can improve wettability by reducing surface roughness, thus enhancing the adhesive's ability to spread and penetrate the wood surface (Leggate et al. 2020; Miklečić et al. 2022). 2.5 Characterization of bonding strength 2.5.1 Tensile strength of FJ wood Properties directly measured via a tensile test include ultimate tensile strength (UTS), breaking strength, maximum elongation, and reduction in area. Tensile tests are performed for various 32 reasons, such as materials development, comparison, selection, and quality control (Patten et al. 2010). The properties derived from tensile tests are used in selecting materials for engineering applications and often help predict or estimate the behavior of a material under different forms of loading (Davis, 2004). Tensile strength parallel to the grain represents the highest property in wood and exceeds the modulus of rupture value (Murata et al. 2011). The mechanical properties of end-grain adhesive-bonded wood joints are critical for the performance of wood-based products, such as CLT. The overall tensile strength of wood products is a complex attribute influenced by both the inherent tensile strength of the wood itself and the tensile strength of any end joints used in a certain application. Understanding these two distinct yet interconnected factors is essential for optimizing the performance of wood in structural applications. Failure mode classification and wood failure percentage are key indicators of FJ bond quality and are widely used as passing criteria in FJ UTS test. According to ASTM D4688 (American Society for Testing and Materials [ASTM], 2021), Failure Modes 2 and 3 are considered unconditionally acceptable, while Modes 4 to 6 are conditionally acceptable, depending on whether the joint meets minimum strength requirements. Dagenais & Salenikovich (2008) adopted the six-mode classification defined in the SPS-4 standard, noting that Modes 1 and 2 reflect adhesive (glue line) failure, while Modes 3 to 5 indicate wood failure within or around the finger profile, representing good bonding performance. Mode 6, which involves 100% wood failure outside the joint area, is generally excluded from analysis. In their study, wood failure ranged from 41% to 85% depending on machining conditions, with only four out of nine series meeting the ANSI A190.1 requirement of ≥60% wood failure for dense hardwoods. Notably, all joints machined at higher cutting speeds (2726 m/min) exceeded this threshold, while the lowest wood failure ratios were linked to improper machining at slower speeds and higher chip loads. Similarly, Bustos et al. (2003) 33 reported over 80% of specimens failing in Modes 3 and 4 with four curing times (1, 2, 5, and 24 hrs), consistent with strong adhesive bonding and proper finger-joint assembly. Mode 3 typically involves shear failure along the joint profile, while Mode 4 reflects tensile failure near finger roots—both associated with high wood failure and considered reliable indicators of joint integrity. These outcomes highlight the importance of proper machining and gluing in achieving high wood failure rates and acceptable failure modes. Furthermore, according to ANSI A190.1- 2022 (American National Standards Institute [ANSI], 2022), a minimum 80% wood failure is required for end-jointed softwoods. When both failure mode distribution and UTS values are evaluated together, they provide a comprehensive measure of FJ performance, allowing determination of whether the joints meet structural standards. 2.5.2 Delamination resistance of bonded joints Timber construction products frequently face extreme climatic conditions, particularly during transport and installation phases (Dietsch et al. 2015). Variations in MC within timber can lead to internal stress, crack formation, and, in bonded products, delamination of adhesive joints (Schaffrath, 2015). Therefore, ensuring high delamination resistance is crucial for the durability of bonded and load-bearing timber products under such extreme stresses. Delamination testing, which accelerates the aging process, assesses bonding resistance for quality control. Delamination is quantified by measuring the total delamination length, excluding outermost gluelines, and dividing it by the total exposed glueline length on the end grain surface, expressed as a percentage. Bond failure between wood and adhesive, driven by the adhesive’s interaction strength with the wood, primarily causes delamination in engineered wood products (Huang, 2010). According to Nadir & Nagarajan (2013), standard ISO 9188-1979 limits average delamination to 5%. 34 Addressing delamination is challenging, especially when bonds face high or fluctuating moisture levels. Wood swells and shrinks with moisture changes, while adhesives, being more hydrophobic and cross-linked, changes less. This discrepancy causes strains at the adhesive-wood interface, which can lead to bond fractures if the wood's swelling is significant, and the resulting strain is not mitigated (Frihart et al. 2015). Delamination testing is therefore essential for FJ wood to ensure the durability and strength of the adhesive bonds under varying environmental conditions. This testing is particularly critical because FJ, which are commonly used in structural applications, can experience significant stress and exposure to moisture, leading to potential failure if not properly bonded (Widmann et al. 2014). Stolze et al. (2021) found that FJ with PRF-bonded and PUR-bonded showing resistance after the climate change during a three-week climate alteration with RH of 80%, 55% and 30% respectively. Nadir & Nagarajan (2013) found that the delamination resistance of rubberwood FJ glued with PRF adhesives which met the exterior exposure durability test specifications, while PVAc-D3 and D4 adhesives failed and exhibited high wet shear strength reduction. FJ made of Sugi laminas treated with low-molecular-weight phenolic (LMWP) resin showed lower delamination percentages compared to untreated lamellas. LMWP-treated laminates have better dimensional stability, reducing stress at bonded interfaces under humidity fluctuations and thus preventing delamination (Hermawan et al. 2013). 2.6 Effect of various factors on mechanical property of bonding 2.6.1 Influence of tool wear Vassileiou (2011) showed that increased knife wear significantly reduced the bending strength, modulus of elasticity, and tensile strength of the FJ silver fir. This reduction in joint strength was 35 attributed to the rougher and less precise cuts made by dull knives, which compromised the surface quality and subsequently the adhesive bond strength. The impact of surface roughness on adhesive tensile strength in end-grain-bonded spruce wood specimens was also investigated by Follrich et al. (2010), who demonstrated that higher surface roughness improves adhesive bond strength by enhancing mechanical interlocking and adhesive penetration into the wood. Rougher surfaces from sawing provide more open cell lumina, allowing adhesives to penetrate and form stronger bonds due to mechanical interlocking where there is more contact between wood adhesives. Furthermore, the study noted that increasing the adhesive spreading quantity also contributed to higher tensile strength, indicating that adequate adhesive coverage is essential for robust bonds. Follrich et al. (2007a) expanded on the importance of adhesive treatment and spreading quality in their study on the mechanical stability of end-grain joints. They found that applying a hydroxymethylated resorcinol primer significantly improved tensile strength, more than doubling it compared to untreated samples. Additionally, increasing the quantity of adhesive spread also enhanced tensile strength, indicating that both chemical treatment of the wood surface and sufficient adhesive application are vital for optimizing the mechanical properties of end-grain joints. Limited information is available in the literature on tool wear on the bonding performance of glued wood products, particularly in terms of delamination resistance. This gap highlights a critical need for further investigation into how tool wear influences the adhesive bond integrity and resistance to delamination, particularly in FJ wood products. 2.6.2 Impact of wood properties One of the primary factors affecting wood's mechanical properties is density. According to Rowell (2005), the mechanical variability of small clear specimens is primarily due to variations in wood 36 density, which itself is influenced by the cell wall/tubular lumen ratio. Nevertheless, density alone is not the sole determinant of wood's mechanical properties. The MFA in the S2 layer of the secondary cell wall also plays a crucial role, particularly in the elastic region (Reiterer et al. 1999; Bergander & Salmén, 2002; Gindl et al. 2004). Lower MFA values correspond to higher longitudinal mechanical properties, especially stiffness (Reiterer et al. 1999; Salmén & de Ruvo, 1985; Barnett & Bonham, 2004). Grain angles are another influential factor. Both macroscopic grain and MFA within the cell wall affect wood's strength properties (Grippa & Mazzolani, 2009). Knots, which form where branches grow from the main stem of a tree, also have a substantial impact on wood's mechanical properties, as they disrupt the normal arrangement of cells and cause stress concentrations (Grippa & Mazzolani, 2009). The relationship between grain orientation and adhesive penetration was examined by Follrich et al. (2007b) in their study on end-grain to longitudinal grain joints in Norway spruce wood specimens. They observed increased adhesive penetration depth in the earlywood. This suggests that the anatomical structure of wood can influence the distribution and effectiveness of adhesive penetration, which is crucial for forming strong bonds. Chemical components are also key factors affecting wood's mechanical properties. Zhang et al. (2013) conducted a study and results revealed that tensile strength and elongation increased as the lignin content decreased. Certain wood species, such as spotted gum and Darwin stringybark, present significant challenges in gluing due to their high density and unique mechanical properties. These species demand deeper adhesive penetration to achieve robust bonds but often fail to meet industry standards because of excessive delamination. Their low porosity and limited permeability hinder effective adhesive penetration, causing adhesives to remain on the surface rather than permeating the wood structure, which is essential for strong bonding (Leggate et al. 2022). Sun et al. (2019) found that high-37 density wood are susceptible to moisture absorption than that of low-density ones, which leads to swelling and shrinkage. These dimensional changes create residual stresses that can compromise the bond and result in delamination in glue-laminated wood products. In glulam configurations, tensile stresses acting on the gluelines during drying exacerbate delamination risks, especially in high-density hardwoods like spotted gum. Factors such as MC fluctuations, drying stresses, and the intrinsic properties of these dense woods further challenge the integrity of the adhesive bonds (Lu et al. 2024). 2.7 Research gap Current research concerning the relationship between tool wear and FJ manufacturing is rather limited, with the majority of existing studies delving into the effects of cutting speed and chip load on tool wear during FJ lumber production. One key conclusion drawn from these studies is that an increase in chip load (the amount of material removed by each cutting edge or tooth of a tool during one revolution) tends to lead to greater tool wear (Hernández et al. 2011). Raknes (1982) also discussed the effects of tool wear on the strength and durability of FJ, suggesting that quality monitoring could be accomplished via visual inspection or power consumption. Vassileiou (2011) furthered this field of study by examining the impact of tool wear over time, measuring usage durations of 1, 32, and 64 hours, and the findings revealed a marked reduction in tensile strength and bending modulus of joints after extended usage. This decrease in strength was primarily attributed to cellular damage caused by tool wear, particularly evident in woods such as FJ ponderosa pine (Reeb et al. 1998). This notion underscores the general trend that as tool wear increases, so does cellular damage, consequently reducing gluing tensile shear strength and wood percent failure in a variety of wood species, including sugar maple wood (Bustos et al. 2010). 38 Although several studies have investigated FJ wood products bonded with PUR adhesives, the current body of research remains concentrated on a narrow set of influencing factors and wood species. Specifically, previous research has predominantly examined the effects of variables such as wood MC (Pommier & Elbez, 2006), end-pressure during joint assembly (Bustos et al., 2003a; Bustos et al., 2011; Rao et al., 2014), adhesive curing time (Bustos et al., 2003a), cutting speed during FJ machining (Bustos et al., 2004; Hernández et al., 2011), and FJ profile configuration (Bustos et al., 2003b; Rao et al., 2012; Morin-Bernard et al., 2020). While these studies have contributed valuable insights into the bonding behavior of PUR adhesives, they have primarily focused on a limited range of species such as maritime pine (Pommier & Elbez, 2006), black spruce (Bustos et al., 2003a, 2003b, 2004, 2011; Hernández et al., 2011), poplar (Timbolmas et al., 2022), and a few hardwood species including white ash, yellow birch, and white oak (Morin-Bernard et al., 2020). However, a significant research gap exists regarding the performance of PUR adhesives in finger-jointed products made from other commercially important North American species, notably DF, HF, and WRC. Despite their widespread industrial use in construction and appearance-grade applications, particularly in North America, little work has been conducted to understand how PUR adhesives interact with the unique anatomical and physical characteristics of these species. Differences in density, extractive content, surface chemistry, and fiber structure among DF, HF, and WRC could significantly influence adhesive wetting, bond formation, and long-term durability which are the factors that have not been systematically evaluated in the context of PUR-bonded FJ assemblies. There is a notable research gap regarding the effect of tool wear and wood properties on the delamination resistance of FJ wood. Further investigation is needed to explore these factors to enhance the durability and reliability of FJ timber products under extreme climatic conditions. 39 This conventional approach overlooks the complexities introduced by irregular grain directions and angled surfaces, such as those encountered in FJ wood products. Consequently, there is a notable lack of studies examining surface roughness and wettability in these contexts. This gap is particularly significant given that the unique surface characteristics of FJ wood can substantially influence adhesive penetration and bond strength, ultimately affecting the performance and durability of engineered wood products. 40 Chapter 3: Materials and methods 3.1 Testing materials Three hundred and thirty kiln-dried flat-sawn 3-m long No. 2&B (National Lumber Grading Authority, 2014) Coastal DF (Pseudotsuga menziesii (Mirb.) Franco), HF – representing a sawmill mix of western hemlock (Tsuga heterophylla (Raf.) Sarg) and amabilis fir (Abies amabilis (Dougl.) Forbes) – and WRC (Thuja plicata Donn ex D. Don) pieces were purchased from local distributors to Vancouver (Canada). Each piece had a nominal 2x4 cross-section corresponding to a 34-mm (radial, R) thickness and 139-mm (tangential, T) width. Each piece was crosscut with a PS50-F manual pendulum saw to a 91.44-cm (longitudinal, L) length that was defect-free over a 5.08-cm L length from both ends. This resulted in 280 specimens per wood species that were randomly assigned to 6 different samples with each containing 140 replicates. The mean basic density (M0%/VGREEN) for DF, HF and WRC was 414.1 ± 59.3kg/m3, 356.1 ± 23.7 kg/m3 and 290.9 ± 27.3 kg/m3, respectively. All specimens were stored in a conditioning room at 20°C and 65% relative humidity until they reached an equilibrium moisture content (EMC) of 12.6%. A full factorial experimental design was devised with two factors and three response variables (Table 3.1), and each sample was assigned a specific FJ treatment. Following FJ, eighty specimens per sample were then glued to produce forty glued replicates according to the specifications of the manufacturer. Twenty FJ glued replicates were then randomly selected and sawn to a length of 1813.8 mm (with the FJ in the center) to test for their UTS according to ASTM D4688 (American Society for Testing and Materials, 2021). Another twenty glued FJ replicates were sawn to a length of 152-mm with the joint at the center, then cross-cut through the center of the joint to let the bond lines on the end-grain exposed and yielding two test specimens in 76 mm long, to measure for delamination according to ASTM D7469-22 (American Society for Testing and Materials, 2023). 41 Another 40 specimens (FJ but not glued) to produce 20 replicates to be glued in a laboratory setting for initial friction UTS testing. The remaining 80 specimens (FJ but not glued) per sample were used to quantify initial friction coefficient of friction and surface quality of the fingers. Table 3.1: List summarizing the independent and dependent variables involved in the full factorial experimental design. Factors Levels Wood species (3 levels) DF (n=280) HF (n=280) WRC (n=280) Tool conditions (2 levels) Sharp (n=420) Worn (n=420) Response variables Levels Surface quality (3 levels) Surface roughness (n=20) Surface energy (n=20) Adhesive wetting rate (n=20) Gluing performances (2 levels) Delamination proportion (n=20) Ultimate tensile strength (n=20) Failure mode assessment (n=20) Initial friction (2 levels) Coefficient of friction (n=20) Ultimate tensile strength (n=20) 3.2 Finger jointing process A Weinig Profitjoint machine was operated for FJ manufacturing within its industrial context. One defect-free end (no knot within 508 mm from end) of each specimen was machined to obtain vertical FJ with a feather profile (Figure 3.1). The geometry of the fingers was 15 mm length, 3.8 mm pitch, 0.86 mm tip and 7.2° slope (Figure 3.2) and the cutterhead had a diameter of 170 mm. The FJs were machined at a rotation speed of 4200 rpm and a feed speed of 15 m/min. Other manufacturing parameters are proprietary to the company where the tests were conducted. During machining, the amperage consumed by the electric motor powering the cutterhead was recorded 42 so the cutting power could be calculated by multiplying this value by the voltage (440 V). Two tool wear levels were used during the tests: freshly sharpened and worn (detailed in Section 3.3). Figure 3.1: Vertical finger jointed specimen of DF made of pieces that were defect-free over the first 50 mm from the glue line. 43 Figure 3.2: Finger geometry with finger length (15mm), pitch (3.8mm), and tip (0.86mm) dimensions. Forty FJ specimens per sample were glued by PUR structural adhesive HB X102 (Henkel) which was applied using a comb. The steady state of the glue application process was visually assessed by experienced operators to ensure an even glueline (Figure 3.3). The adhesive was applied according to the technical recommendations supplied by the glue manufacturer (Appendix A). An end pressure of 10 MPa was applied for a duration of 10 seconds. The remaining 20 specimens per sample were only FJ (not glued) to assess initial friction and surface quality. 44 Figure 3.3: Adhesive was applied on half the fingers (from tip to mid-finger length) and the glue application (uniformity and quantity) was assessed by experienced operators prior to FJ with SPF (spruce-pine-fir) wood samples. 3.3 Tool wear measurement In the FJ process, edge recession, nose width and edge radius were adopted to evaluate tool wear (Figure 3.4). To classify the wear, the optical microscope (Zeiss JENAMED2, Epifluorescence brightfield) was used with a magnification of 3.5x. The measurements were taken when the tools were freshly sharpened (Figure 3.5a) and after FJ green WRC within its industrial context (Figure 3.5b). This approach ensures accurate measurement of the tool wear characteristics and helps in understanding the effect of tool condition during FJ manufacturing. 45 Figure 3.4: Tool wear measurements done for the FJ knives: cutting edge radius, edge recession (ER) and nose width (NW). Figure 3.5: Tool condition measurement for (a) freshly sharpened tool and (b) worn tool after FJ green WRC within its industrial context. 3.4 Surface quality evaluation 3.4.1 Surface roughness The surface roughness was measured on defect-free areas with an Olympus LEXT OLS 4000 Confocal Laser Microscope. An area of 50 mm2 located at the center of a finger (FJ machining surface) was scanned. Three fingers per specimen were scanned. The arithmetical mean deviation 46 of the profile (SA), the maximum profile peak height (SP) and the maximum pit height (SV) (Figure 3.6). The definition of these three surface roughness parameters are described in Table 3.2 and their respective equations listed in Table 3.3 according to ISO 25178 (British Standards Institution, 2025). Figure 3.6: Example schematic of SA, SP and SV. Table 3.2: Definition of each surface roughness parameters. Parameters Definition SA Expresses as an absolute value, the difference in height of each point compared to the arithmetical mean of the surface. This parameter is used generally to evaluate surface roughness. SP The height of the highest peak within the defined area. SV The absolute value of the height of the largest pit within the defined area. Table 3.3: Roughness parameters according to equation from ISO 25178. Parameters Equations 𝑺𝑨 𝑆𝐴 =1𝐴∬ |𝑍(𝑥, 𝑦)|𝑑𝑥𝑑𝑦𝐴 𝑺𝑷 𝑆𝑃 =𝑚𝑎𝑥𝐴 𝑧(𝑋, 𝑦) 47 𝑺𝑽 𝑆𝑉 =𝑚𝑖𝑛𝐴 𝑧(𝑋, 𝑦) 3.4.2 Wettability and surface energy The wettability was quantified by measuring the contact angle (θ) of a drop of PUR adhesive on a defect-free areas of a finger within 24 hours following the machining treatments. The contact angles were defined as the angle between the solid surface and a tangent line drawn through the point where atmosphere, liquid and solid meet (Zisman, 1963). The contact angles were measured with Theta Flex 300-Pulsating Drop 200 Tensiometer at 20°C. A frame grabber recorded the changes in droplet profile during the first 60 s of wetting. A 5 µl drops of liquid was placed on the finger surface using the micropipette. The high-speed camera captured the image of the drops every 1 s during the first 60 s and contact angles were calculated as a mean of both sides of the drop to compensate for any longitudinal variations. Measurements were carried out in the longitudinal direction since wetting is more constant in the longitudinal direction (Gardner et al. 1991; de Meijer et al. 2000). Wetting rate was calculated as ∆𝜃∆𝑡 to assess the spreading and penetration of the adhesive. The change of contact angle was plotted versus time (60 s) to explore the dynamic wetting property of each wood species machined with sharp and worn tool. The surface energy was determined by measuring the initial contact angle of three liquids (water, ethylene glycol and glycerol). One finger per liquid per specimen was used to obtain the initial contact angle using the Theta Flex 300-Pulsating Drop 200 Tensiometer at 20°C. The measurements were done within the first 24 hours following the FJ manufacturing. As discussed in the literature review, the acid-base (vOCG) approach delivers the most detailed information about surface chemistry, in giving values for acid and base components in addition to polar and disperse components of surface free energy, which is especially valuable with a chemically heterogeneous material like wood (Gindl et al. 2001). The vOCG approach provides 48 for greater accuracy for calculating the surface free energy of wood composites in many studies than the other existing methods and therefore recommended (Gardner 1996; Wålinder & Gardner, 2002; Gindl et al. 2001). The Young’s equation (Equation 3.1) is the basis of the method to calculate the SFE by using the contact angle data of the tested liquids. 𝛾𝑆 = 𝛾𝑆𝐿 + 𝛾𝐿𝑐𝑜𝑠𝜃 (3.1) Where 𝛾𝑆 is the surface tension on the solid-gas interphase, 𝛾𝑆𝐿 is the surface tension on the solid-liquid interphase. 𝛾𝐿 is the surface tension on the liquid-gas interphase and 𝜃 is the contact angle of the tested liquids. In the classical treatment of surface interactions, the total contribution is subdivided into a dispersion part and a polar part (Järnström et al. 2007) which illustrate in Equations 3.2 and 3.3 𝛾𝑆 = 𝛾𝑆𝐷 + 𝛾𝑆𝑃 (3.2) 𝛾𝐿 = 𝛾𝐿𝐷 + 𝛾𝐿𝑃 (3.3) In the vOCG approach, the polar component (hydrogen bond component) was described as 𝛾𝑃 but change to 𝛾𝐴𝐵 , where AB is acid-base interactions which is a combination of contributions from electron donors (𝛾−) and electron acceptors (𝛾+). The sum of the acid-base components can then be redefined as 𝛾𝐴𝐵 = 2√𝛾+ + 𝛾− . The non-polar (dispersion) term was described as 𝛾𝐷 but changed to 𝛾𝐿𝑊 , where LW refers to all London-van der Waals forces. Thus, the surface free energy could be described as 𝛾 = 𝛾𝐴𝐵 + 𝛾𝐿𝑊. The interfacial tension between the solid and liquid interface can then be defined by Equation 3.4: 𝛾𝑆𝐿 = 𝛾𝑆 + 𝛾𝐿 − 2√𝛾𝑆𝐿𝑊𝛾𝐿𝐿𝑊 − 2√𝛾𝑆+𝛾𝐿− − 2√𝛾𝑆−𝛾𝐿+ (3.4) Combining Equations 3.1 and 3.4 results in Equation 3.5: 𝛾𝐿(1 + 𝑐𝑜𝑠𝜃) = 2√𝛾𝑆𝐿𝑊𝛾𝐿𝐿𝑊 + 2√𝛾𝑆+𝛾𝐿− + 2√𝛾𝑆−𝛾𝐿+ (3.5) 49 As there are three unknown terms relating to the solid surface, the contact angle data of solid wetting with distilled water (polar), ethylene glycol (non-polar) and glycerol (polar) was measured. The acid-base values of test liquids used according to vOCG have been listed in Table 3.4 (Järnström et al. 2007). Table 3.4: The surface tension components of the specific probe liquids (≈20 °C). Probe liquid 𝜸𝑳 𝜸𝑳𝑳𝑾 𝜸𝑨𝑩 𝜸𝑳+ 𝜸𝑳− Water 72.8 21.8 51.0 25.5 25.5 Ethylene Glycol 48.0 29.0 19.0 1.92 47.0 Glycerol 64.0 34.0 30.0 3.92 57.4 With three unknown terms, the solid surface, surface free energy for solid surface can be calculated by Equation 3.6 (Järnström et al. 2007): 𝛾𝑆𝐿 = 𝛾𝑆𝐿𝑊 + 2√𝛾𝑆+𝛾𝑆− (3.6) 3.5 Gluing performance tests To assess the mechanical properties of the FJ, tensile strength tests, along with the identification of the failure mode, and delamination tests were conducted. 3.5.1 Ultimate tensile strength test After FJ, all glued specimens were stored in a conditioning room at 20°C and 65% relative humidity until tested. Tensile tests were performed according to ASTM D4688 (American Society for Testing and Materials, 2021) and SPS-1 2014 (National Lumber Grading Authority, 2014) using an Metriguard 403 Tension Proof Tester. The UTS was determined along with the failure mode, which was determined by carefully examining the area of failure and classifying the type 50 failure based on Table 3.5 in accordance with ASTM D4688 (American Society for Testing and Materials, 2021). Table 3.5: Failure Mode Criteria from ASTM D4688 (American Society for Testing and Materials, 2021). Mode Description Schematic examples Specimen examples 1 Failure mostly along the glueline surfaces of the joint profile with poor wood failure of any kind (wood failure<70%) 2 Failure mostly along the glueline surfaces of the joint profile with good wood shear failure (wood failure>70%) 3 Failure mostly along the joint surface but with some failure at the finger roots or scarf tips. Good overall wood shear failure along the joint profile surfaces. 4 Mostly tensile wood failure at the fingerjoint roots or scarf tips and with high overall wood failure. Little failure of any kind along the joint profile. 5 Failure beginning at the joint (possibly due to a stress riser) and progressing away from the joint. Essentially 100% wood failure. 6 Failure away from the joint (not influence by the joint)-all wood failure. 51 3.5.2 Cyclic delamination test Cyclic delamination tests were conducted based on ASTM D7469-22 (American Society for Testing and Materials, 2023). The test specimens were stacked in the pressure vessel and submerged in water having a temperature of 30°C±3°C. A vacuum of 600 mm Hg was drawn and held for 30 min then released, after which a pressure of 600 kPa was applied for 2 hours. The test specimens were then dried in oven at 71°C ± 3°C for 12 hours. The length of the delamination was measured by feeler gauge (0.1 mm thickness) immediately after drying and was divided by the total length of exposed bond line to obtain the proportion of the delamination. Example of glue line delamination as shown in Figure 3.7 (Stolze et al. 2021). Figure 3.7: Delamination of glue line after cycle of treatments. 3.5.3 Microscopic observations Small cubes of 1 cm x 1 cm x 1 cm were cut to observe the FJ surfaces to describe the effect of the machining process on the surface in terms of cellular damage, level of fibrillation and open lumens. Similarly, small cubes were also cut from glued FJ surfaces to visualize the adhesive penetration. Samples used to visualize cell damage and/or glue penetration were prepared with a 52 razor blade mounted onto a microtome by carefully cutting one of the end-grain surfaces. An optical microscope (Zeiss JENAMED2, Epifluorescence brightfield) was also used to take micrographs for three representative FJ specimens for each condition and wood species. The optical microscope (Zeiss JENAMED2, Epifluorescence brightfield) was also used to measure the glueline thickness of FJ specimens with minimum, maximum and average UTS from each sample. To do so, the joint area of each selected specimens was cross-cut at the center to let the glue line on the end-grain expose and the glue line thickness were measured using an optical microscope. 3.6 Initial friction measurements To evaluate the impact the two independent variables on glued but not yet cured FJ lamellas to maintain their assembly integrity, initial friction (IF) measurements were conducted. Static friction tests were conducted 5 minutes after the adhesive application to measure the coefficient of friction (CoF) by using a CoF testing equipment with an angle adjustment system (Figure 3.8) to describe the frictional behavior between two pieces during the first stages of the assembly when the glue is still in its liquid state. These tests were based on ASTM G219-23 to assess the static friction between FJ finger surfaces. 53 Figure 3.8: Schematic of (a) rider and incline surface and (b) coefficient of static friction tester. To simulate the frictional behavior of FJ, two sets of saw blades (sharp and worn) were used to rip cut 20 replicates from each species at a 4.8° from the longitudinal axis - the same as the fingers – thus exposing a surface similar to that of FJ surfaces in this study (Figure 3.8). A DIY setup utilizing a jack stand was employed to implement the inclined plane approach for measuring the coefficient of CoF between two pieces. The upper plate, which supports the test sample, was attached only at its upper left surface to the jack stand. As the screw of the jack was twisted, the left side of the plate was lifted while the right side remained unsupported, creating a tilting motion. This setup allowed for a gradual increase in inclination, effectively mimicking a controlled inclined plane. To determine the CoF, one of the pieces was placed on the upper plate, while the other piece remained stationary. By continuously raising the jack stand, the inclination of the upper plate increased until the test sample began to slide. The critical angle (θ) at which the motion initiated was recorded and the static coefficient of friction (μs) was calculated as μs=tan(θ). Tensile tests were also conducted 5 minutes after the PUR adhesive was applied on freshly FJ lamellas with the goal to measure the tensile force required to separate an uncured FJ (based on 54 ASTM D4688 (American Society for Testing and Materials, 2021). This was done to simulate stress undergone by the lamella during the FJ manufacturing process. The results from the initial friction measures will be related to the surface quality assessment (Section 3.4) and the mechanical properties test (Section 3.5). It is hypothesized that the initial friction measurements would correlate with the UTS, surface roughness and/or the wettability. The statistical analysis that will be performed will also serve to assess if the initial friction is impacted by the three FJ parameters under study in a similar way to the other properties obtained from mechanical properties and surface quality assessment. 3.7 Statistical analysis This experiment followed a full factorial experimental design. Analysis of variance (ANOVA) was used to evaluate the effects of two independent variables: two tool wear levels (freshly sharpened and worn) and three wood species (DF, HF and WRC) on each dependent variable ((surface roughness, adhesive wetting rate, surface energy, CoF, and UTS with uncured adhesive, UTS with cured adhesive, delamination resistance) using the general linear model in SPSS software (SPSS 29). The means and standard deviations of the dependent variable’s outputs were calculated. Linearity and normality tests were done for datasets. Tukey test was performed to compare the means when significant interactions were detected. 55 Chapter 4: Results and Discussions 4.1 Surface topography The ANOVA results for the surface topography indicate that both tool condition and wood species significantly influenced SA, SV, and SP (Table 4.1). For SA, SV and SP, the single effects of tool condition and wood species were significant, and the interaction of tool condition and wood species was only significant for SP. The F-value associated with the tool conditions were higher for SP and SV, indicating the stronger effect of tool wear for these surface roughness parameters (Table 4.1). In comparison, SA was mostly affected by the wood species, due to the unique anatomical characteristics of each wood species. In this discussion, we used SA to quantify the effect of wood species on FJ surface roughness, while SV and SP will mainly be used to assess the effect of tool wear on FJ surface roughness. Table 4.1: Analysis of variance (ANOVA), including the F- and p-values for three surface roughness parameters characterizing FJ surfaces of three wood species prepared with freshly sharpened and worn cutting tools. A 95% confidence level is indicated by *, while ** was used to highlight a 99% confidence level. Sources SA SP SV F-value p-value F-value p-value F-value p-value Intercept 9279.1 0.000** 10027.2 0.000** 6514.5 0.000** Tool condition (TC) 8.7 0.004** 144.5 0.000** 9.5 0.003** Wood species (S) 34.2 0.000** 8.1 0.001** 3.5 0.032* TC×S 0.9 0.405 5.1 0.008** 0.6 0.532 Effect of wood species This study highlights the influence of wood species on surface roughness, emphasizing the role of wood density, anatomy, and fiber orientation. Denser wood tends to produce smoother surfaces due to its greater resistance to mechanical degradation (Jankowska, 2020; Pereira et al. 2018). Surface roughness varies depending on the cutting direction, as fibers on the radial section are cut at different angles, resulting in higher roughness compared to the tangential section (Jankowska, 2020). Additionally, roughness is greater when measured perpendicular to the grain than parallel 56 to the grain, as fiber orientation affects cutting resistance and surface texture (Jankowska, 2020; Kang et al. 2023; Chavenetidou & Kamperidou, 2024). The presence of larger vessel and pore diameters also contributes to increased surface roughness, as these structural features create irregularities on the wood surface (Jankowska, 2020; Kang et al. 2023; Gurău, 2014). When interpreting the results for SA, it can be observed that WRC specimens were characterized by the highest SA values followed by DF and HF (Table 4.2, Figure 4.1). Statistically, WRC and DF surfaces were not significantly different and exhibited greater SA values than that of HF (Table 4.3). These findings are surprising because of the difference in wood density between both wood species (378.2 kg/m3 vs. 538.3 kg/m3 for WRC and DF, respectively) and wood anatomy (Bawcombe, 2012; Awoyemi & Jones, 2011). On the one hand, WRC could have generated relatively rough surfaces due to its lower density and greater susceptibility to torn grain (Anjos et al. 2014). WRC SA values were also associated with larger standard errors of the mean (Table 4.2) and spread in the data (Figure 4.1), which suggest some variability in surface quality within samples. This could be due to the machining direction with respect to the grain, which was previously reported to impact surface characteristics in black spruce wood cants machined with chipper-canters (Hernández et al. 2013). When FJ, the overall 90°-90° cutting direction (cutting perpendicular to the grain) is associated with more pronounced surface irregularities (Iskra & Hernández, 2009) and surface defects (Woodson and Koch, 1970). Similar irregularities, created by a group of tracheid cells being torn from the surface, were observed at the microscopic level (Figure 4.2c and f). This caused a wave-like pattern on the surface, that was more apparent on the specimens prepared with sharp cutting tools due to their ability to clean wood cells (Figure 4.2c). When using worn cutting tools, some groups of tracheid cells remained attached to the surface (Figure 4.2f), but this effect could have been emphasized by the orientation of the rays. The 57 relationship between the depth of cellular damage and ray orientation was previously discussed by Busto et al. (2004) in FJ black spruce wood. Table 4.2: Mean surface roughness parameters (standard deviation of the mean in parenthesis) of specimens from three different wood species FJ with either freshly sharpened or worn cutting tools. Mean (n=20) within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. Tool condition Wood species SA SP SV Sharp DF 14.5 (1.2) 232.8 (15.5) a 176.0 (19.9) HF 12.9 (1.8) 238.6 (21.1) a 170.2 (17.7) WRC 16.4 (2.2) 268.5 (28.2) b 185.3 (19.3) Worn DF 15.8 (1.2) 324.4 (32.5) d 197.3 (30.6) HF 14.0 (1.5) 296.3 (25.9) c 181.6 (33.3) WRC 16.8 (2.0) 321.2 (45.5) d 194.9 (25.4) Figure 4.1: Box plots of the mean surface roughness parameter (SA) for FJ specimens from three different wood species (Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC)) machined using sharp and worn cutting tools. The middle line represents the median, the "X" marks the mean, and the box spans the interquartile range (IQR). Whiskers extend to 1.5 times the IQR, and dots indicate outliers. Table 4.3: Effect of wood species for SA and SV (tool conditions pooled; n=40). Means in a column followed by the same lowercase letter are not statistically different at the 95% confidence level. Wood species SA SV DF 15.1 (1.4) b 186.6 (27.7) a HF 13.4 (1.7) a 175.9 (26.9) ab WRC 16.6 (2.1) c 190.1 (22.8) b 58 Figure 4.2: Transverse optical microscopy micrographs of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ surfaces machined with freshly sharpened and worn cutting tools. 59 On the other hand, the greater SA values of DF FJ surfaces were likely caused by the distinct difference in wood density between early- and latewood bands within the annual growth rings. While not as apparent as for WRC, a slight wave-like pattern created by the tearing of smaller groups of tracheid cells can be observed for DF surfaces prepared with sharp (Figure 4.2a) and worn (Figure 4.2d) cutting tools. In Figure 4.3, surface irregularities in earlywood appeared as micro-fuzzy grain, where small particles remained partially attached rather than being cleanly severed during machining, causing them to stand above the cutting plane. This was not observed in latewood, which is consistent with findings reported by Sandak et al. (2017). Deviations from the expected cutting plane indicate that groups of cells were removed, i.e., tracheid were pulled out, leading to gaps resembling micro-torn grain (Figure 4.4), similar to observations reported by Follrich et al. (2010) in their study on flawless spruce. As shown in Figure 4.3 and Figure 4.4, surface irregularities were more prevalent in earlywood given that their cell walls are thinner than that of latewood, aligning with the results reported by River et al. (1991). Because earlywood comprises a larger proportion of the growth rings in DF, it can be hypothesized that surface irregularities are determined by how the thinner-walled tracheid cells are fractured during the FJ process. For comparison, the wave-like surfaces formed by the micro-torn grain or the removal of groups of cells in WRC and DF can be seen in Figure 4.5. 60 Figure 4.3: Three-dimensional rendition the surface of a FJ specimen of Douglas-fir machined with sharp cutting tools. Magnification=200x. Figure 4.4: Transverse optical microscopy micrograph of a FJ specimen of Douglas-fir wood machined with sharp cutting tools. Magnification=250x. 61 Figure 4.5: Topography of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ surfaces machined with freshly sharpened and worn cutting tools. The images were acquired by the confocal microscope used to do the surface roughness measurements. In contrast, HF surfaces had the lowest SA values when FJ for both sharp and worn cutting tools. Its intermediate density and its uniform grain structure (Heshmati et al. 2018) tended to result in a smoother machined surface, where no cell wall deformation or cell tear-out could be observed (Figure 4.2b and e). In other words, FJ made of HF yielded surfaces having a smoother appearance after FJ compared with DF and WRC (Figure 4.5b). Effect of tool wear According to the data, worn cutting tools generated FJ surfaces characterized with higher roughness values than that of sharp knives (Table 4.2, Table 4.4). This was likely caused by the greater cutting forces, both normal and parallel, that increased with tool wear (Kolar et al. 2016; Bendikiene & Keturakis, 2016; Aknouche et al. 2009; Keturakis et al. 2017; Guo et al. 2021). As shown in Figure 3.5, tool wear had a significant effect on the cutting edge geometry. Specifically, the average cutting edge radius increased from 0.015 mm to 0.19 mm, resulting in an increase in the contact area of the rear flank face (Adesta et al. 2009). This corresponded to an increase in 62 friction, which is evidenced by the higher values of cutting power recorded during the FJ manufacturing process (30.1 kW and 15.9 kW for worn and freshly sharpened tools, respectively; Appendix B). According to Pinkowski et al. (2024), tool wear could have altered chip formation and surface quality. This occurs as the blunt cutting edge removes individual cells or groups of cells below the cutting plane, cutting forces generally increase (Pinkowski et al. 2024). When tool wear is important, it is also possible to observe cell crushing, which may generate surfaces that appear uniform (Carrano et al. 2004; McAllister, 2022) but limit coating (Richter et al. 1995) and adhesive (Follrich et al. 2007; Cool & Hernández, 2011) penetration. In this study, cellular damage was observed in the form of cell wall deformation, but crushing was absent (Figure 4.2). When interpreting the results for SP, and SV, significant increase under the worn tool conditions for all wood species can be observed (Table 4.2, Table 4.4, Figure 4.6). These two surface roughness parameters are somewhat related via the mean line of the profile, according to ISO 21920: 2021 (International Organization for Standardization, 2021) and provide information on surface irregularities complementary to that provided by SA. The increase in both SP and SV with tool wear demonstrate that the change in cutting edge radius led to more pronounced wood surface irregularities (Bendikiene & Keturakis, 2016). Based on Figure 4.5, both valleys and peaks were generated on the FJ surfaces. Table 4.4: Effect of tool wear for SA and SV (wood species pooled; n=60). Means in a column followed by the same lowercase letter are not statistically different at the 95% confidence level. Tool condition SA SV Sharp 14.6 (2.3) a 177.2 (19.7) a Worn 15.5 (2.0) b 191.3 (30.3) b 63 Figure 4.6: Box plots of the (a) maximum peak height (SP) and (b) maximum valley depth (SP) for FJ specimens from three different wood species (Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC)) machined using sharp and worn cutting tools. The middle line represents the median, the "X" marks the mean, and the box spans the interquartile range (IQR). Whiskers extend to 1.5 times the IQR, and dots indicate outliers. While the wave-like pattern mentioned above could be observed when FJ DF with freshly sharpened knives, it was prevalent in DF and WRC when using worn tools (Figure 4.5) due to the larger cutting-edge radius and increased cutting forces (Bendikiene & Keturakis, 2016). The wave within DF growth rings can be related to early- and latewood cells, where the former appear below the cutting plane, or the mean profile line and the latter are above (Figure 4.5a). The combination of the cutting direction, which fosters the removal of groups of cells, and the easier removal of thin earlywood cell walls, induced this wave-like pattern that was amplified by tool wear (Figure 4.5d). This can also be explained by the marked difference in wood density between early- and latewood (350 kg/m³ vs. 800 kg/m³ for early- and latewood, respectively according to Abdel-Gadir et al. 1993). In HF, this wave-like pattern was more subtle due to the very narrow band of latewood 64 cells (Figure 4.5e). It was, therefore, the earlywood cells that mostly impacted the surface quality of HF FJ surfaces, which contributed to smaller surface roughness parameters (Table 4.2, Table 4.3). Namely, the SV value (175.9 μm in Table 4.3) was similar to that of the sharp tool condition (177.2 μm in Table 4.4) and SP values were amongst the lowest both with sharp and worn cutting tools (Table 4.2). This suggests that the HF surfaces generated during the FJ process were more uniform compared to those of DF (Figure 4.5). In WRC, the effect of tool wear was more evenly distributed across growth rings (Figure 4.5). This was likely caused by its lower wood density and mechanical properties (378.2 kg/m3 vs. 462.8 kg/m3 vs. 538.3 kg/m3 for WRC, HF and DF, respectively). When assessing the machinability of WRC according to ASTM D1666 (ASTM International, 2022), Williams & Morris (1998) reported this wood species performed relatively poorly when boring and mortising. Those two machining processes involve a similar cutting direction to that of the FJ process used in this study. The peaks detected by SP in DF when FJ with worn tool was likely caused by higher level of tool wear that could have remove earlywood cells due to its lower density than that of latewood cells (350 kg/m³ earlywood vs. 800 kg/m³ latewood (Abdel-Gadir et al. 1993). As a result, these thinner earlywood cells are more susceptible to cell deformation during machining by worn tool, creating a wavy appearance on the fingers (Figure 4.2). It was reported that a worn tool can result in a smoother surface in low-density wood due to the compression, which can reduce surface irregularities and raised grain (Pinkowski et al. 2024). While opposite results of WRC (lower density) found in this study were likely due to the different milling processes and cutting directions used in this study and the one from Pinkowski et al. (2024). 65 Conclusion This section demonstrated that both wood species and tool condition significantly influenced the surface topography of FJ lamellas. Worn cutting tools consistently increased roughness parameters (SA, SP, SV), with the most effects observed in WRC due to its lower density. DF and HF exhibited increases in roughness with tool wear, reflecting their denser and more uniform fiber structure. Microscopic and 3D surface analyses confirmed that worn tools introduced surface irregularities such as micro-torn grains, micro fuzzy grains and wave-like textures, which may impact adhesive spread and penetration. Achieving a balance between surface topography for mechanical interlocking and smoothness for adhesive wettability is essential for producing high-quality FJ. 4.2 Surface energy and PUR wetting rate Understanding the surface energy of wood surfaces is crucial for estimating its chemical adhesion in with adhesives. This section investigates how tool conditions and wood species influenced the dispersive (𝜸𝑺𝑳𝑾), acidic (𝜸𝑺+) and basic components (𝜸𝑺−), as well as the total surface energy (𝜸𝑺𝑳). In addition, the wetting of PUR, measured using the contact angle, will be used to provide complementary information. The objective of this analysis is to evaluate the effects caused by two tool conditions on wood's interaction with a PUR adhesive along with the properties and characteristics of the three wood species under study. The analysis begins with an investigation of 𝜸𝑺𝑳𝑾, which underlies the properties of non-polar interactions and hydrophobicity. This is followed by an examination of 𝜸𝑺+ and 𝜸𝑺_− components, which respectively define the electron-accepting and electron-donating properties of the surface. The 𝜸𝑺𝑳 is then evaluated, integrating the contributions of all surface energy components. Finally, the wetting rate is analyzed to determine 66 how the surface topography and surface energy influence PUR adhesive spreading, penetration and bond formation. The ANOVA results indicate the tool conditions and wood species significantly influenced all components of the surface energy as well as the 𝜸𝑺𝑳 and wetting rate (Table 4.5). For 𝜸𝑺𝑳𝑾, 𝜸𝑺+, 𝜸𝑺− and 𝜸𝑺𝑳, the single effects of tool condition and wood species were significant; indicating that tool sharpness influenced the exposure of chemical composition like lignin and hydroxyl group availability. The interaction between tool conditions and wood species significantly influenced the surface energy components and 𝜸𝑺𝑳, showing that different wood species respond uniquely to tool wear; however, the interaction between tool conditions and wood species was not significant for the wetting rate (Table 4.5). The F-value associated with the wood species was larger for all surface energy components and 𝜸𝑺𝑳, indicating the stronger effect of species-related factors, such as wood anatomical and chemical characteristics of each species on these surface energy parameters (Table 4.5). In comparison, wetting rate was mostly affected by the tool conditions, due to the impact of tool wear on surface topography (Section 4.1). Table 4.5: Analysis of variance (ANOVA), including the F- and p-values for dispersive component, acidic component, basic component, total surface energy and adhesive wetting rate of FJ surfaces of three wood species prepared with freshly sharpened and worn cutting tools. A 95% confidence level is indicated by *, while ** was used to highlight a 99% confidence level. Sources 𝜸𝑺𝑳𝑾 𝜸𝑺+ 𝜸𝑺− 𝜸𝑺𝑳 Wetting rate F-value p-value F-value p-value F-value p-value F-value p-value F-value p-value Intercept 29380.2 0.001** 645.4 0.001** 19.9 0.001** 28797.1 0.001** 673.6 0.000** TC 68.62 0.001** 69.3 0.001** 65.9 0.001** 69.6 0.001** 11.34 0.001** S 514.9 0.001** 512.3 0.001** 451.6 0.001** 572.6 0.001** 4.3 0.015* TC×S 17.8 0.001** 17.9 0.001** 17.2 0.001** 11.3 0.001** 1.9 0.147 67 4.2.1 Surface energy Dispersive component (𝜸𝑺𝑳𝑾) According to the results, DF exhibited the highest 𝜸𝑺𝑳𝑾 when cutting with both tool conditions, indicating DF surfaces were significantly more hydrophilic than that of HF and WRC (Table 4.6) due to higher Lifshitz-van der Waals force (i.e., London dispersion force). These increase cohesion and enhance the intermolecular interaction between wood and liquid due to temporary fluctuations in electron density around molecules, which induce instantaneous dipoles (Bonn et al. 2001). Discrepancies in 𝜸𝑺𝑳𝑾 values obtained in this study (Table 4.6) and the ones published by de Meijer et al. (2000) could be attributed to the testing liquids selected for the experiments, where water, ethylene glycol and glycerol were used in our study while water, formamide and diiodomethane were used in the study of de Meijer et al. (2000). However, the trend is similar with the 𝜸𝑺𝑳𝑾component contributing most to the total surface energy. Table 4.6: Mean dispersive (𝜸𝑺𝑳𝑾), acidic (𝜸𝑺+) and basic (𝜸𝑺−) components and total surface energy (𝜸𝑺𝑳) (standard deviation in parenthesis) of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. Tool conditions Wood species 𝜸𝑺𝑳𝑾(mJ/m2) 𝜸𝑺+(mJ/m2) 𝜸𝑺−(mJ/m2) 𝜸𝑺𝑳(mJ/m2) Sharp DF 93.7 (3.1) d -5.8 (0.7) a -2.8 (0.7) a 101.7 (4.7) e HF 86.7 (4.2) c -4.25 (0.8) c -1.6 (0.7) b 91.8 (5.7) d WRC 89.9 (2.1) c -5.04 (0.4) b -2.1 (0.4) b 96.4 (2.8) d Worn DF 80.5 (6.8) b -2.9 (1.6) d -0.6 (1.2) c 84.1 (6.6) c HF 62.0 (7.4) a 1.4 (1.8) f 2.7 (1.3) d 66.9 (7.1) a WRC 64.6 (4.6) a 0.6 (1.0) e 2.2 (0.9) d 67.3 (4.8) b WRC's intermediate 𝜸𝑺𝑳𝑾 under sharp tools (Table 4.6) indicate its surface can support strong London dispersion force if machined properly. However, WRC’s significant decrease in 𝜸𝑺𝑳𝑾 when cutting with worn tools, which was statistically similar to that of HF (Table 4.6), would 68 produce more hydrophobic surfaces allowing lesser water uptake. This was likely due to cellular damage caused by the worn tool (e.g., cell deformation) and a reduction in available bonding sites. Similar findings were reported for Norway spruce by Qin & Holmbom (2007). Additionally, WRC has a high extractive content (Stirling & Morris, 2011) that can mask the polar nature of the wood surface. For example, it has been reported that the removal of extractives exposes underlying polar functional groups that alter the surface properties (Liu et al. 1998). Wood porosity influences 𝜸𝑺𝑳𝑾 by affecting its hydrophilic properties as it facilitates water penetration and movement (Varivodina et al. 2010). DF had the highest 𝜸𝑺𝑳𝑾, while HF showed lower, which corresponds to their average porosity values (40.1% for DF (Standfest et al. 2012) vs. 37.3% for HF (Jang et al. 2020). Sharp tools produced higher 𝜸𝑺𝑳𝑾 in all species (Table 4.6) due to cleaner cuts, producing a more uniform, and chemically stable surface. The 𝜸𝑺𝑳𝑾 decreased significantly when cutting with worn tool, this could be attributed to increased fiber damage and surface roughness which reduce non-polar interactions by interfering with molecular attractions at the interface (Benjamin, 2020). Worn tools may also expose more porous and cause more irregular surfaces, trapping airborne contaminants that interfere with adhesive bonding. Since van der Waals forces depend on close molecular contact (Righetti & Boschetti, 2013), worn tools can cause surface irregularities when machining wood species with low density and low mechanical properties, which may disrupt these forces and may form a weak boundary layer that further interferes with dispersion bonding (Rajupet et al. 2021), similar findings reported by Rajupet et al. (2021) who confirmed the rougher substrate decrease the Van der Waals force. 69 Acidic (𝜸𝑺+) and basic (𝜸𝑺−) components The 𝜸𝑺+ represents the electron accepting ability of the wood machined surface. Under the sharp tool condition, all wood species showed negative 𝜸𝑺+, meaning these surfaces behaved more as electron donors and were characterized by a lower acidity. This may be interpreted as a negative contribution to the total surface energy. Similar negative values of acidic and basic components were reported by de Meijer et al. (2000), although most studies have positive values (Mantanis & Young, 1997; Wei & Yushin, 2012; Qin et al. 2014; Gardner, 2007). DF had the lowest 𝜸𝑺+, followed by WRC and HF (Table 4.6) regardless of the tool condition. The intermediate 𝜸𝑺+ of WRC might be due to its high content of extractives which was migrated to the wood surface caused by the tool wear and following oxidation (Yu et al. 2023). The results show the 𝜸𝑺+ of HF and WRC increased dramatically to positive values except DF when cutting with from sharp tool to worn tool, which shows the overall higher acidity of those species. High acidity observed on wood surfaces indicates greater electron-accepting capacity (Gardner, 2007). These surfaces are generally more hydrophilic and tend to react with electron-donating substances, which can enhance interactions with certain polar adhesives. The 𝜸𝑺− remained negative under sharp tool conditions for all species, with DF showing the lowest 𝜸𝑺− while WRC and HF had statistically similar values (Table 4.6). The negative value of 𝜸𝑺− is likely due to the very high contact angle of water, which is true due to the non-wetting meniscus (De Meijer et al. 2000). The 𝜸𝑺− of WRC and HF increased and shifted to positive values when cutting with worn tool, whereas DF remained negative with minimal change. Theoretically, the basic groups are produced by the rearrangement of the groups between wood and oxygen from air (Yu et al. 2023). The increased 𝜸𝑺− when cutting with worn tool might be caused by greater exposure of the contact area with air. Higher 𝜸𝑺− indicates that wood surfaces had a greater electron-donating capacity (Gardner, 2007) 70 and can readily form hydrogen bonds and interact with electron-accepting (acidic) adhesives or liquids. The 𝜸𝑺+ and 𝜸𝑺− together represent the polar component of surface energy, describing the surface’s capacity for polar interactions. Although their individual trends and statistical patterns differ, both are influenced by similar mechanisms, including mechanical degradation, tool wear friction, oxidation, and chemical composition exposure. Due to the shared influencing factors and overlapping explanatory mechanisms, the “polar components” will be used in the rest of the discussion to refer to 𝜸𝑺+ and 𝜸𝑺− when discussing surface polarity and its effects on wettability and adhesion performance. The acidity in wood can be attributed to the presence and accessibility of chemical composition, e.g., cellulose, hemicellulose and lignin. Hydroxyl groups in cellulose and hemicellulose can act as both weak acid and weak base (Tamura et al. 1989), which enhance the hydrophilicity of wood (Khazraji & Robert, 2013; Oh & Park, 2021). Additionally, cellulose can participate in acidic proton donation, which contributes to the action of polar components. This process involves the hydroxyl groups on the cellulose backbone acting as proton donors and facilitating the breakdown of the glycosidic bonds in acidic environments (Saharay et al. 2010; Matsuoka et al. 2011; Alves et al. 2016; Lelekakis et al. 2014). Distinct chemical structure of hemicellulose introduces additional acidic functionalities, which contribute to the polar components and increasing the surface energy of wood and influencing wettability and adhesion properties (Liu et al. 1997; Lundqvist & Ödberg, 1996). Its carboxylic acid groups further enhance wood surface acidity and polar components, contributing to a more uniform surface energy distribution and reducing hysteresis effects in wetting measurements (Luner & Sandell, 2007; Tshabalala, 1997; Gellerstedt & Gatenholm, 1999). The aromatic structure from lignin is known to promote oxidative 71 depolymerization and facilitate the formation of quinones and carbonyl functionalities (Yao et al. 2018). These oxidation products act as electron acceptors, triggering autoxidation reactions that increase surface acidity (More et al. 2021; Jyothibasu et al. 2022) and polar components. According to Figure 4.7, all three wood species have similar content of cellulose, hemicellulose and lignin. Based on previous studies on the effect of wood chemical compositions on the polar components discussed above, the three wood species should theoretically show the close value of polar components. However, the results from our study contradict these expectations in which HF showed the highest polar components, followed by WRC and DF. This trend can be explained by additional factors. Among the three investigated wood species, DF had the highest density (538.3 kg/m³) and greater mechanical strength (Schaffer, 1973), an intermediate surface roughness (Table 4.2), and less surface damage when cutting with worn tool. The combination of these factors could have limited the exposure of cellulose and hemicellulose on the surface, thereby leading to the lowest measured polar component. In contrast, HF underwent a dramatic increase in its polar component when FJ with sharp to worn cutting tools with a similar trend observed in the surface roughness analysis (Figure 4.1 and Figure 4.6) in Section 4.1. The smoothest surface (i.e., lowest SA) of HF suggests a lower potential for exposure of cellulose and hemicellulose on the surface. However, its high polar component is likely due to its relatively high hemicellulose content (Figure 4.7), which contain hydroxyl groups. WRC having the lowest density and highest surface roughness among the three species made it most likely respond to mechanical degradation, but it exhibited an intermediate polar component. This unexpected behavior can be attributed to its higher extractive content, particularly thujaplicins and water-soluble phenolic compounds (Stirling & Morris, 2011), which may contribute to electron-donor behavior that would influence the polar component. 72 Figure 4.7: Percentages of cellulose, hemicellulose, and lignin in Douglas fir, Hem-fir and Western Redcedar (Lewis, 1950). Worn tools increase surface roughness (as discussed in Section 4.1) and expose more hydroxyl-rich cellulose, which increase the polar components of wood surface. Mechanical degradation caused by tool wear may disrupt the lignocellulose structure, which may allow water-soluble extractives (such as phenolics) to migrate to the surface and contribute to a significant increase in the polar component. Repeated mechanical action of tools disrupts the aromatic structure of lignin, which acts as electron acceptors and increases the polar component. The study by Zhang et al. (2019) reported that the increase in temperature causes the carboxylic acid groups to decompose and generate carbonyl and quinone groups, following oxidation which significantly increases the electron accepting capacities (EAC). The heat generated on the wood surface when machining with worn cutting tools further enhances the role of carbonyl and quinone groups in increasing the EAC of the wood surface. In contrast, sharp tools minimize mechanochemical disruption and heat 73 generation, thereby reducing the extent of oxidation and preserving the electron-donating behavior of lignin, leading to lower surface acidity. Total surface energy The results indicate that sharp tool produced higher 𝜸𝑺𝑳 across all wood species compared to worn tools (Table 4.6). Among the wood species under study, DF exhibited the highest 𝜸𝑺𝑳, followed by WRC and HF under both tool conditions (Table 4.6). The 𝜸𝑺𝑳 is composed of 𝛾𝑆𝐿𝑊 and polar components, each contributing differently with the 𝛾𝑆𝐿𝑊 being the dominant contributor to 𝜸𝑺𝑳, which was supported by Mantanis & Young (1997). According to Equation 3.6 in Section 3.5.2, 𝛾𝑆𝐿𝑊 is an independent additive term, whereas the contribution of polar components depends on the geometric mean of their values. This structure inherently weights 𝛾𝑆𝐿𝑊 more heavily, meaning that even if polar components change significantly, their influence on 𝛾𝑆𝐿 is limited unless both 𝛾𝑆+ and 𝛾𝑆− are high. The trends in total surface energy correlate with surface roughness parameters (SA, SV, SP) discussed in Section 4.1. Higher surface roughness (SA, SV, SP) resulting from worn tools corresponded to a decrease in 𝜸𝑺𝑳 across all species. Surface irregularities disrupt liquids spreading and reduce molecular contact, leading to lower overall surface energy. DF exhibited the highest 𝛾𝑆𝐿 under both tool conditions (Table 4.6) which is attributed to its higher density and lower extractive content, provided a chemically stable and reactive surface and reduced susceptibility to mechanical degradation contributed to higher 𝛾𝑆𝐿. In comparison, WRC exhibited intermediate 𝛾𝑆𝐿 values, despite having the roughest surface, which was associated with low density and poor machining performance (Williams & Morris, 1998). The lower 𝛾𝑆𝐿 of WRC is likely a result of its high extractive content (Stirling & Morris, 2011), which masks functional groups and introduces hydrophobic barriers that reduce adhesive-wood interactions (Nussbaum & 74 Sterley, 2001). HF exhibited the lowest 𝛾𝑆𝐿, although it had the smoothest surface which indicates that surface roughness alone did not govern total surface energy. Sharp tools facilitated smoother surfaces (Table 4.6), maximizing van der Waals interactions and increasing 𝛾𝑆𝐿𝑊. Worn tools significantly reduced 𝛾𝑆𝐿𝑊 (Table 4.6), due to increased fiber damage and surface irregularities (micro-torn grain and micro-fuzzy grain) that disrupted nonpolar molecular interactions thus weaker the dispersive components. The local thermal effects were generated due to friction when cutting with worn tool, which can degrade the chemical integrity of the wood surface thus reducing liquid interactions (Christiansen, 1990a, 1990b) and adhesive bonding efficiency (Murmanis et al. 1983; Sergeevichev et al. 2022). 4.2.2 Wetting rate Sharp tools produced higher wetting rates across all species compared to worn tools (Table 4.7). Among the three wood species, HF exhibited the highest wetting rate, followed by DF and WRC (Table 4.8). Under sharp tool conditions, the PUR wetting rate over the first 60 sec of wetting was similar (Table 4.9). However, when using worn tools, DF and WRC experienced a decrease in wetting rate, whereas HF maintained a relatively high value (Table 4.9). Table 4.7: Mean wetting rate (standard deviation in parenthesis) of FJ lamellas made by freshly sharpened and worn cutting tools for all wood species. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. Tool conditions Wetting rate (°/s) Sharp 0.33 (0.11) a Worn 0.26 (0.14) b Table 4.8: Mean wetting rate (standard deviation in parenthesis) of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with both freshly sharpened or worn cutting tools. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. Species Wetting rate (°/s) DF 0.28 (0.13) a HF 0.34 (0.13) b WRC 0.26 (0.13) c 75 Table 4.9: Mean wetting rate (standard deviation in parenthesis) of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. Tool conditions Wood species Wetting rate (°/s) Sharp DF 0.34 (0.14) HF 0.35 (0.09) WRC 0.32 (0.12) Worn DF 0.23 (0.10) HF 0.33 (0.17) WRC 0.21 (0.12) HF exhibited the highest wetting rate when cutting regardless of the tool conditions, which can be attributed to its intermediate density and uniform porosity, allowing consistent adhesive distribution and penetration (Heshmati et al. 2018). DF showed intermediate wetting rates under sharp tools but experienced a significant reduction with worn tool. This behavior is linked to its large earlywood-latewood density variation, which created inconsistent roughness patterns that affected adhesive diffusion (Hecker & Becker, 1995). Additionally, high density wood could limit adhesive absorption, as reported by Karlinasari et al. (2021) for eucalyptus wood. In this study, DF’s higher density led to a reduced wetting rate, which was associated with an increase in machining defects (i.e., wave-like surface pattern) with tool wear. WRC exhibited the lowest wetting rate under all conditions, likely due to its higher extractive content, which can reduce surface energy by forming hydrophobic barriers that hinder liquid spreading and penetration (Wålinder & Johansson, 2001; Taylor, 2004), causing uneven bonding (Rezaei et al. 2019). The wetting rate decreased when using from sharp tool to worn tool correlates with the increased surface roughness (SA, SV, SP) (Section 4.1). Previous studies indicate that increased roughness from tool wear prevents adhesive flow and penetration (Neese et al. 2004; Koran & Vasishth, 1972; DeVallance et al. 2007; Ünsal, 2009) by creating surface irregularities (wave pattern and 76 fuzzy grain) and cellular damage. The increase in SP observed in DF and WRC when cutting with worn tool (Table 4.2, Figure 4.6) supports this observation, where higher peak could have hindered adhesive spreading (Ji et al. 2024). Surface energy fundamentally contributes to the interaction between wood surface and adhesives by modulating the wetting behavior of the adhesive film (Korpela et al. 2025; Zhao et al. 2023; Xiao et al. 2024). While higher 𝜸𝑺𝑳 typically enhances wettability, its effect is weak and largely depends on the relative contributions of the surface’s Lifshitz–van der Waals component ( 𝛾𝑆𝐿𝑊) and its polar components. Among the studied wood species, the 𝛾𝑆𝐿𝑊 dominated the 𝜸𝑺𝑳, indicating strong nonpolar dispersive interactions, however, this component alone did not effectively promote adhesive wetting. This is because van der Waals forces (especially London dispersion forces) contributes weak and non-directional adhesion (Wang et al. 2022), and do not initiate chemical curing between PUR and wood surfaces. Instead, polar interactions enabled hydrogen bonding (Oertel & Abele, 1985; Blackwell et al. 1981) and covalent (chemical) bonding (Rath et al. 2008) during the PUR initial curing process. HF showed the highest polar components, which significantly enhanced wettability. The abundance of hydroxyl groups (–OH) on the HF surface (primarily from cellulose and hemicellulose) not only enabled hydrogen bonding with polar groups in PUR (e.g., –NH, –CO, and residual –OH groups), but also directly participated in reactions with isocyanate groups (–NCO) in PUR to form strong polar covalent urethane linkages (–NH–CO–O–) (Wang et al. 2023). These interactions are both directional and permanent, contributing to the formation of a chemically integrated adhesive interface. DF showed a relatively high non-polar component but a lower polar component, and its wettability under sharp tool conditions was supported by smooth surface rather than polar chemical bonding. However, under worn tool conditions, increased surface roughness and lower polar reactivity 77 reduced adhesive flow, limiting wetting and interfacial cure reactions. WRC presented a unique case with a higher polar component but poor surface wettability. This may be due to its high extractive content, which forms a hydrophobic, non-polar barrier that limited the accessibility of hydrophilic –OH groups, thereby limiting both hydrogen bonding and covalent urethane formation with PUR. While van der Waals interactions (especially dispersion forces) can still occur between the hydrophobic extractives and PUR's alkyl chains, such nonpolar interactions lack the strength and specificity required for durable bonding. Wetting curve analysis supports these findings (Figure 4.8). For all three species, initial contact angles varied among the tool conditions, but final angles converged within 60 seconds. This indicates prolonged binder flow that compensated for and initial surface energy differences. DF and HF exhibited exponential decreases in contact angle, indicating faster adhesive diffusion due to a balance between porosity and surface roughness which followed the results from Table 4.8. WRC showed a more gradual, linear decrease in contact angle, likely due to higher extract content which slowed adhesive penetration. Figure 4.8: Contact angle evolution with wetting time for (a) Douglas fir, (b) Hem-fir and (c) Western Redcedar when cutting with sharp tool and worn tool. Error bar shows the standard error of the mean. 78 4.2.3 Conclusion This analysis highlights the significant influence of tool conditions and wood species on surface energy components and wettability, demonstrating how machining affects wood’s adhesive spreading potential. Sharp tools produced higher 𝜸𝑺𝑳𝑾, 𝜸𝑺𝑳, and wetting rates, facilitating stronger van der Waals interactions and better adhesive spreading. In contrast, worn tools increased surface roughness and mechanical degradation, leading to reduced 𝜸𝑺𝑳𝑾 and 𝜸𝑺𝑳 but increased polar components, particularly in HF and WRC. These findings suggest that tool wear could enhance the exposure of hydroxyl-rich cellulose and hemicellulose, modifying electron-donating and electron-accepting behavior, while also disrupting uniform liquid spreading. The results further emphasize that wood density, porosity, and extractive content play a crucial role in determining the extent of these effects. 4.3 Initial friction (IF) This section presents the analysis of IF characteristics of FJ lamellas during the pre-cured stage of bonding. The objective of this section is to evaluate the IF-UTS and IF-CoF of the joints when the adhesive is uncured as well as assess the ability of freshly pressed FJ pieces to resist external mechanical disturbances such as misalignment, shifting, or separation, especially as they are transferred along assembly lines before the adhesive has fully cured. The analysis is structured into two main parts. Section 4.3.1 focuses on IF-UTS, which reflects the axial load resistance of the joint during its uncured state. Section 4.3.2 focuses on IF-CoF, which characterizes the static friction between wood surfaces prior to curing. For both response variables, the effects of wood species, tool condition, and relevant surface characteristics including surface roughness, surface energy, and adhesive wetting rate are considered. 79 The ANOVA results for the IF properties showed the single effect of tool condition was significant on both IF-UTS and IF-CoF (Table 4.10). While wood species had no significant single effect on IF-UTS, it significantly affected IF-CoF (Table 4.10), indicating that variations in density, porosity, and anatomical structure affect resistance of misalignments and separation during joint assembly due to various initial adhesive cohesion. Additionally, the effect of interaction between tool condition and wood species was significant for both IF-UTS and IF-CoF, suggesting that machining effects differ across wood species. The F-value associated with tool conditions was the largest for both IF-UTS and IF-CoF (Table 4.10), indicating stronger effects of tool wear for these IF parameters. Table 4.10: Analysis of variance (ANOVA), including the F- and p-values for IF-UTS and IF-CoF of FJ lamellas of three wood species prepared with freshly sharpened and worn cutting tools. A 95% confidence level is indicated by *, while ** was used to highlight a 99% confidence level. Sources IF-UTS IF-CoF F-value p-value F-value p-value Intercept 326.413 0.000** 13935.6 0.000** Tool condition (TC) 158.668 0.000** 2616.154 0.000** Wood species (S) 0.463 0.63 11.437 0.000** TC×S 4.036 0.020* 15.052 0.000** 4.3.1 Initial friction – ultimate tensile strength (IF-UTS) Effect of wood species Although the IF-UTS values were statistically similar among the three wood species, slight variations were observed across species. Under sharp tool conditions, DF exhibited a relatively low IF-UTS compared to that of WRC and HF (Table 4.11, Figure 4.9). While DF had an intermediate SA, (Table 4.2) but its surface was characterized by smooth and rough areas of late- and earlywood, respectively (Figure 4.3). The presence of these smooth earlywood areas limited early mechanical interlocking of the uncured PUR. DF having high surface energy that promotes 80 good adhesive spread, however, it had the lowest IF-UTS under sharp tools. Similar trend found on HF and WRC, which had relatively high total surface energy and wetting but lower IF-UTS, this reinforcing the idea that surface texture plays a dominant role in early-stage joint stability. WRC’s porous structure and overall rougher surface (highest SA) promoted mechanical anchorage in the uncured state of adhesive and favored the interlocking of the FJ surfaces. Table 4.11: Mean initial friction ultimate tensile strength (IF-UTS) and mean initial friction coefficient of friction (IF-CoF) (standard deviation of the mean in parenthesis) of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. Tool conditions Wood species IF-UTS (MPa) IF-CoF Sharp DF 1.00 (0.81) c 0.29 (0.02) d HF 2.56 (3.05) c 0.42 (0.04) b WRC 2.80 (1.56) c 0.35 (0.02) c Worn DF 13.56 (6.89) a 0.89 (0.07) a HF 11.94 (5.58) ab 0.88 (0.06) a WRC 10.15 (3.43) b 0.91 (0.07) a Figure 4.9: Box plot showing the effect of wood species (Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC)) on the mean (a) IF-UTS, (b) IF-CoF under sharp tool and worn tool conditions. The middle line represents the median, the "X" marks the mean, and the box spans the interquartile range (IQR). Whiskers extend to 1.5 times the IQR, and dots indicate outliers. 81 Effect of tool conditions When sharp cutting tools were used, the machined surfaces were smoother, which minimized frictional resistance and resulted in lower IF-UTS values across all wood species (Table 4.10). This indicates that sharp tools produce less surface irregularities that promote mechanical interlocking (Spaggiari & Dragoni, 2013; Warcholiński & Gilewicz, 2022), making joints more reliant on adhesive cohesion rather than frictional resistance (Israelachvili et al. 1999; Zarei et al. 2020). In contrast, worn tools induced greater surface roughness with greater fiber exposure, thus increasing surface area and enabling more effective interlocking at the wood-adhesive interface, which maintained the integrity of the assembly prior to curing before curing (Durmaz et al. 2020; Ma et al. 2023). 4.3.2 Initial friction - coefficient of friction (IF-CoF) Although limited research exists on the frictional behavior between an uncured glued assembly of wood surfaces, studies on dry wood friction provide meaningful insights. In our results, IF-CoF was significantly higher in specimens machined with worn tools across all wood species (Table 4.10), which corresponds to an increased surface roughness (Table 4.2). Under sharp tools, smoother surfaces promoted better PUR adhesive spreading (Table 4.8). Similar results reported by Altgen et al. (2016) and Jaic et al. (2013) for beech wood and urea-formaldehyde (UF) adhesives. However, machining with worn cutting tools introduced micro-torn and fuzzy grain (see Section 4.1). This likely disrupted adhesive flow and enhanced mechanical interlocking at the microscale, which became a dominant contributor to friction (Xu et al. 2014). As a result, the use of worn cutting tools substantially increased the frictional resistance, and all three wood species demonstrated similarly high IF-CoF values. This is consistent with the findings of Xu et al. (2014), 82 who reported that friction coefficients increase linearly with surface roughness due to a reduction in the actual contact area and an increase in mechanical interlocking. Among the three species, HF and WRC exhibited higher IF-CoF values compared to DF (Table 4.9). This result corresponds to their surface roughness profiles; WRC had the highest roughness parameters (SA, SV, SP), followed by DF and HF (Table 4.2). HF had the smoothest surface topography but showed high IF-CoF likely due to larger pore diameter, less cell damage, and highest polar components of surface energy which increased the adhesive wetting rate and promoted adhesive penetration thus enhancing mechanical interlocking with the adhesive. In contrast, DF presented the lowest IF-CoF under sharp tool conditions, but highest when cutting with worn tool, this shift was likely attributed to the rougher surfaces disrupting adhesive spreading and creating more resistance between wood substrates with adhesive. 4.3.3 Conclusion The evaluation of initial friction characteristics revealed that tool condition plays a dominant role in influencing both IF-UTS and IF-CoF of uncured FJ lamellas. Worn tools significantly increased both IF-UTS and IF-CoF across all wood species, primarily due to increased surface roughness which enhanced mechanical interlocking. In contrast, sharp tools produced smoother surfaces, resulting in lower initial frictional resistance and greater dependence on adhesive cohesion for joint stability. While wood species had a limited effect on IF-UTS, it showed slight differences under sharp tool, reflecting the effect of differences in surface texture, porosity, and anatomical structure. For the FJ industry, it is recommended to carefully balance machining parameters to ensure adequate frictional resistance during the uncured phase without excessively compromising adhesive wetting or long-term bond strength. In high-speed production lines where jointed lamellas 83 must maintain alignment and integrity before curing, moderate surface roughness can be achieved through controlled tool wear or light texturing which may improve handling stability. However, excessive roughness should be avoided, as it may hinder adhesive distribution and increase the risk of premature failure after curing. 4.4 Gluing performances The objective of this section is to assess the mechanical performance of FJ lamellas by analyzing their UTS and glueline delamination. This analysis aims to determine how cutting tool condition, wood species, and their interaction influence bond integrity and structural reliability of FJ products. This study also seeks to identify the effects surface topography, surface energy and wetting rate on the strength and durability of adhesive joints formed using a PUR adhesive. The section is structured in two parts: Section 4.4.1 discusses UTS to investigate the influence of wood species, tool wear levels, and their interaction on joint strength; Section 4.4.2 discusses delamination proportion as an indicator of bond durability and resistance to environmental stress. The ANOVA results for mechanical properties indicate that only the single effect of wood species had a significant influence on UTS (Table 4.12). The interaction between tool condition and wood species was also significant for UTS, indicating that the influence of tool wear on mechanical performance is species dependent. For the proportion of glueline delamination, none of the independent variables (i.e., tool condition and wood species) nor their interaction had a significant effect. 84 Table 4.12: Analysis of variance (ANOVA), including the F- and p-values for UTS and glueline delamination of FJ lamellas of three wood species prepared with freshly sharpened and worn cutting tools. A 95% confidence level is indicated by *, while ** was used to highlight a 99% confidence level. Sources UTS Glueline delamination F-value p-value F-value p-value Intercept 1635.89 0.000** 41.53 0.000** Tool condition (TC) 0.02 0.90 2.56 0.11 Wood species (S) 3.43 0.036* 0.80 0.45 TC×S 4.76 0.010* 0.26 0.77 4.4.1 Ultimate tensile strength FJ lamellas prepared with sharp tools were defined by UTS values that were statistically similar across the three wood species (Table 4.13, Figure 4.10). When using worn cutting tools, DF FJ lamellas were characterized by a significantly higher UTS compared to that of HF and WRC, (Table 4.13). Furthermore, DF FJ lamellas exhibited an increase –although not significant statistically– in UTS when machining with worn cutting tools, while HF and WRC showed a significant decrease in UTS (Table 4.13, Figure 4.10). Table 4.13: Mean ultimate tensile strength (UTS) and glueline delamination (standard deviation in parenthesis) of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. Mean within a column followed by a same lowercase letter are not significantly different at the 95% confidence level. Tool conditions Wood species UTS (MPa) Glueline delamination (%) Sharp DF 129.6 (37.5) a 0.84 (0.84) HF 130.8 (31.3) a 2.25 (2.87) WRC 133.7 (35.7) a 0.92 (2.36) Worn DF 156.2 (45.8) a 2.44 (4.14) HF 116.9 (30.7) b 2.69 (5.20) WRC 118.6 (19.8) b 2.11 (4.72) 85 Figure 4.10: Box plot showing the effect of wood species (Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC)) on the mean (a) UTS, (b) glueline delamination under sharp tool and worn tool conditions. The middle line represents the median, the "X" marks the mean, and the box spans the interquartile range (IQR). Whiskers extend to 1.5 times the IQR, and dots indicate outliers. As shown in Figure 4.11, the number of adhesive failures that happened directly on the glue line (Failure Mode 1) was much lower than the number of failures classified as Modes 3 to 6, which occurred along the joint profile or in the wood itself. In total, 50 specimens failed in Mode 2 and Mode 3, which is higher than the number of failures in any of Modes 4 to 6. According to ASTM D4688 (American Society for Testing and Materials [ASTM], 2021), Failure Modes 2 and 3 are considered unconditionally acceptable, while Modes 4 to 6 are conditionally acceptable depending on the strength of the joint. As reported Bustos et al. (2003), observing wood failures >70% (corresponding to Mode 2 and higher in our study) confirms the FJ process was done properly. In parallel, the ANSI A190.1-2022 (American National Standards Institute [ANSI], 2022) requires that at least 80% wood failure (corresponding to Mode 3 and higher in our study) for end-jointed softwoods. In this study, most failures were in Mode 3 or higher, which meets this requirement. 86 Figure 4.11: Bar graph showing the frequency distribution of Failure Modes observed for each FJ lamellas tested in tension. All samples are pooled regardless of tool condition and wood species (n=120). Besides the failure mode results, the SPS-1 (National Lumber Grades Authority [NLGA], 2023) and SPS-4 (NLGA, 2020) standards require a minimum UTS of 18.1 MPa and 27.9 MPa, respectively to meet the highest quality grade for FJ products. In this study, the mean UTS values of all specimens from all treatment groups were much higher than both standards (Table 4.13); further demonstrate that the FJ process conducted in this study was effective and produced strong, good-quality FJ wood products that meet or exceed industry standards. The mean UTS value for FJ lamellas with Failure Modes 2 and 3 (136.0 MPa) was also used to benchmark whether conditionally acceptable Failure Modes 4 to 6 could perform comparably (Figure 4.12). Based on this approach, data points above the dashed line in Figure 4.12 may be considered to have UTS strength stronger than that of FJ lamellas that failed according to Failure Modes 2 and 3, while those below indicates a reduced bond integrity. Only DF FJ lamellas prepared with worn tools that 87 failed according to Failure Modes 4 and 5 had UTS values superior to the 136.0 MPa benchmark (Figure 4.12) would be considered to have satisfactory bond strength in tension. Figure 4.12: Mean UTS of specimens with failure modes 4, 5, and 6 appear on the FJ lamellas made from Douglas-fir (DF), Hem-Fir (HF), and Western Redcedar (WRC) under sharp and worn tool conditions. The dashed line represents the mean UTS of FJ lamellas that failed according to Failure Modes 2 and 3. The dashed line represents the mean UTS of FJ lamellas that failed according to Failure Modes 2 and 3 Effect of wood species The UTS value for the three wood species were statistically similar when cutting with a sharp tool (Table 4.12). While the effect of tool wear was not significant for DF (Table 4.8), these FJ lamellas underwent a 20.5% increase in their UTS values when using worn cutting tools. This unexpected increase in UTS could be due to enhanced mechanical interlocking enabled by rougher surfaces (Table 4.2) (Azari et al. 2010; Du & Zhu, 2022). Other authors have reported similar findings when testing the tensile-shear strength of beech wood (Hänsel et al. 2023), shear strength of meranti, 88 kapur and pine wood (Hiziroglu et al. 2013a) and bending strength of nyatoh, white oak, and pine wood (Hiziroglu et al. 2013b). Mechanical interlocking is also increased by adhesive penetration, which was greater and more uniform in DF specimens (Figure 4.13a and d). This agrees with earlier findings that deeper and more uniform adhesive penetration typically promotes higher UTS (Johnson, 1985; Woodward & Minor, 1988). An improved mechanical adhesion could also explain why the DF FJ lamellas prepared with worn tools that failed according to Modes 4-5 were considered acceptable. Additionally, DF displayed the highest 𝜸𝑺𝑳 when cutting with both sharp and worn tools which formed its hydrophilic surface. Even though the effect of tool wear resulted in a significant decrease in 𝜸𝑺𝑳 and 𝜸𝑺𝑳𝑾 which made the surface more hydrophobic, it still contributed to enhance adhesive compatibility and bond strength under both sharp and worn tool conditions. 89 Figure 4.13: Representative cross-section image showing the adhesive penetration in Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ lamellas machined with freshly sharpened and worn cutting tools. The images were acquired using an optical microscope. 90 In contrast, HF FJ lamellas underwent a 10.6% decrease in their UTS when machining with worn tools, and an increase in Mode 4 failures (wood failure away from glueline; Table 3.2) was observed (Figure 4.14). This could be due to the overall smoother surfaces (Table 4.2), which were favored by the uniform grain structure (Heshmati et al. 2018). This likely contributed to the consistent adhesive distribution that yielded a uniform and thick glueline compared to that of DF (Table 4.14). The thicker glueline also indicates a shallower penetration of the adhesive in the specimens, which is supported by Figure 4.14. The main surface energy components also decreased when using a worn tool, indicating that both mechanical and chemical adhesion declined when using worn cutting tools in FJ HF. Figure 4.14: Proportion of failure modes appear on the FJ lamellas made from Douglas-fir (DF), Hem-Fir (HF), and Western Redcedar (WRC) under sharp and worn tool conditions. The percentages represent the proportion of each failure mode within cutting treatment. 91 Table 4.14: Mean glueline thickness of three FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. Tool condition Species Glueline thickness (mm) DF 0.12 Sharp HF 0.21 WRC 0.20 DF 0.16 Worn HF 0.18 WRC 0.19 WRC exhibited distinct behavior compared to DF and HF. When FJ with worn tools, WRC showed an 11.3% decrease in UTS but a notable increase in Mode 3 failures (Figure 4.14), which is considered unconditionally acceptable. WRC has the lowest density among the three species studied (378.2 kg/m³ compared to 462.8 kg/m³ for HF and 538.3 kg/m³ for DF). Lower density is typically associated with larger porous size and larger lumen spaces, which can facilitate better adhesive penetration and mechanical interlocking (Yelle & Stirgus, 2016; Kamke & Lee, 2007). Under worn tool conditions, increased surface roughness likely further enhanced adhesive flow into the porous structure of WRC, promoting stronger mechanical interlocking at the bond interface. The glue line thickness in WRC specimens was maintained consistently below 0.3 mm, which is the recommended limit for PUR structural bonds to prevent cavity formation and bond weakening (Richter & Steiger, 2005; Sterley et al. 2004). However, Henkel Canada Corporation recommends a maximum glueline thickness of 0.1mm (Appendix A). Effect of tool conditions Under sharp tools, failure Mode 6 (Figure 4.14) was dominant, particularly in DF and WRC, suggesting that the joint strength exceeded the cohesive strength of the surrounding wood. When worn tools were used, the occurrence of Mode 6 failures in WRC decreased, while Mode 3 failures 92 (considered as desirable performance) increased notably from 10% to 30%. This shift indicates that tool wear improved mechanical interlocking due to the higher surface roughness across all species. This enhanced bonding performance in WRC, resulting in a higher proportion of acceptable failures (Mode 3) due to the occurrence of stress concentrations along the wood-adhesive interface (Persson, 2023). Similarly, DF also showed an increase in Mode 3 failures (Figure 4.14) under worn conditions associated with increased UTS which indicates that the gain in mechanical interlocking outweighed the negative impact of increased roughness. Wetting rate was also a significant factor. HF had the highest wetting rate under both tool conditions, attributed to its uniform porosity and high 𝜸𝑆+ (electron-accepting) component, which facilitated adhesive spreading. While wetting supports adhesive flow, inconsistent and lower adhesive penetration depth (Figure 4.13 d and e) critically reduced the bond strength or UTS of FJ HF lamellas (Table 4.13). WRC showed the lowest wetting rate, likely due to hydrophobic extractives such as thujaplicins and water-soluble phenolic compounds (Stirling & Morris, 2011) which may mask the functional group (Liu et al. 1998), thus altering adhesive flow and penetration (Figure 4.13), and ultimately negatively impacting UTS (Table 4.13). 4.4.2 Glueline delamination analysis When interpreting the results for the proportion of glueline delamination, none of the independent variables (tool conditions and wood species) had a statistical effect (Table 4.12), which contrasts with several previous findings. For example, significant differences in glueline delamination were reported for ash, beech, oak, spruce, poplar, maple, larch, yellow poplar, and rose gum wood (Luedtke et al. 2015; Das et al. 2023; Song & Kim 2022; Pröller et al. 2018). However, in our study, such differences were not apparent and the overall proportion of glueline delamination was 93 less than the values reported in those studies, which indicated the superior performance of the PUR adhesive used and the overall outstanding quality of the glueline. PUR adhesives are known for their high ductility and ability to absorb stress within the joint (Marra, 1992; Wetzig et al. 2011), which allows the adhesive to accommodate for the dimensional changes undergone by the shrinking and swelling of the wood samples (Ross & Service, 2010; Purba et al. 2022). While PRF adhesives are more sensitive to moisture and temperature once cured and hardened (George et al. 2003; Umemura et al. 2002), PUR systems remain flexible under moderate environmental fluctuations, which may have further contributed to the uniform performance observed across all variables. In this study, the glue line thicknesses of all measured FJ lamellas were below 0.3 mm (Table 4.14), which is the recommended maximum for PUR structural applications to avoid cavity formation and bond weakening (Richter & Steiger, 2005; Sterley et al. 2004). This controlled and uniform glueline likely minimized the effect of wood species and tool conditions on delamination performance, ensuring stable and durable bonding across all treatment groups. Effect of wood species The hygroscopic and anisotropic nature of wood causes dimensional changes in radial, tangential, and longitudinal directions when MC fluctuates, which induces internal stresses at the glueline (Peck, 1957; Stamm, 1935). Denser woods with higher specific gravity tend to swell and shrink more under wet–dry cycles, generating larger strain-induced stresses at the glue line (Frihart, 2006a). When adhesives lack the ability to absorb or dissipate these stresses (particularly stiff or brittle adhesives), bond failure is more likely to occur at the adhesive–wood interface (Frihart, 2003, 2006b). Furthermore, studies have shown that volumetric swelling, shrinkage, and growth 94 ring patterns also influence bond performance. Woods with higher swelling capacity or tension wood content tend to exhibit greater dimensional instability (& de Zeeuw, 1980; Daniel et al. 2006). However, in this study, there were no statistically significant differences found in delamination proportions among the wood species and the anatomical differences had minimal impact on bond performance. This outcome can be attributed to the use of PUR adhesive, which is known for its ductility and flexibility, that likely compensated for differences in wood anatomy by absorbing dimensional stresses and reducing stress concentration at the bond line. Second, the glue line thickness in all specimens was consistently below the recommended threshold of 0.3 mm (Richter & Steiger, 2005; Sterley et al. 2004), ensuring uniform adhesive distribution and minimizing the influence of wood species on delamination performance. Effect of tool conditions When FJ with worn tools, the proportion of glueline delamination increased for all species comparatively to using freshly sharpened knives. While these changes were not statistically significant, they suggest that surface irregularities, previously characterized as a wave-like pattern (Figure 4.5), induced or enhanced by tool wear may begin to influence adhesive performance under cyclic stress. In other words, surface roughness might play a role in delamination behavior. As discussed in Section 4.1, sharp tools produce smoother surfaces with minimal surface irregularities, allowing the adhesive to spread more evenly and form stronger bonds. Thus, reducing the likelihood of delamination during cyclic testing (Persson, 2023). The sufficient spread rate of the PUR adhesive along with its high ductility likely mitigated the effects of surface degradation and prevented widespread adhesive failure (Marra, 1992). The limited impact of 95 machining conditions may also be due to the strong performance of the glueline in resisting failure under environmental stresses (Wetzig et al. 2011). The PUR adhesive used in this study demonstrates high performance on smooth and clean wood surfaces due to its reliance on surface contact for effective curing (Burchardt, 2010; Yan et al. 2011; Herzele et al. 2018). The surface energy supports adhesive wettability and bonding (Korpela et al. 2025; Zhao et al. 2023; Xiao et al. 2024), and its reduction can impair glueline durability under stress. This can be supported by the findings in Chapter 4.2. Higher surface energy, particularly the polar component, promotes stronger chemical interactions between wood and PUR adhesive, reducing the possibility of glueline delamination. DF exhibited the highest total surface energy, but lower polar component (𝜸𝑆+ and 𝜸𝑆−), indicating that bonding of FJ DF lamellas relied more on surface smoothness and adhesive flow, assisted by end-pressure during FJ manufacturing. In contrast, HF showed the increased delamination when cutting with worn tool can be linked to the reduction in surface polar groups due to surface damage from tool wear, limiting both hydrogen bonding and covalent bond formation. WRC's delamination behavior was influenced by the presence of surface extractives, which formed hydrophobic barriers that restricted adhesive wetting and chemical bonding, despite a relatively higher polar component. 4.4.3 Conclusion The results of this section addressed the objective by clarifying how wood species, tool wear, and their interaction influence the mechanical performance of FJ lamellas. UTS was significantly affected by both wood species and the interaction between species and tool condition, indicating that tool conditions change surface quality combining with different wood properties effect mechanical properties. DF showed improved UTS under worn tool conditions, likely due to 96 enhanced mechanical interlocking, while HF and WRC experienced UTS reductions associated with surface degradation. In contrast, delamination proportion was not significantly influenced by any variable, suggesting that the PUR adhesive used provided consistently high bond integrity across all conditions. These findings confirm that both material selection and machining quality play critical roles in bond performance. For industrial applications, DF is recommended when tool wear is expected, while maintaining sharp tools is essential for achieving reliable joints in lower-density species like WRC. The continued use of PUR adhesives is also encouraged, as their high adaptability to surface variation and excellent mechanical performance make them suitable for diverse wood substrates and machining conditions. 4.5 Implementation and recommendation for industry The findings of this study offer insights into industrial manufacturers aiming to enhance the early-stage integrity, performance, and durability of FJ products using PUR adhesive. This section translates the experimental results into practical guidelines for machining operations and species selection to reach outstanding performance. Because UTS is one of the most important dependent variables for industrial manufacturers, most of the following discussion is centered on maximizing this property. The mechanical performance and bonding quality achieved in this study surpassed both industry standards and values reported in prior literature. Most specimens failed Failure Modes 3 or higher, which meets the ANSI A190.1-2022 requirement that 80% of the failure area occurs in the wood. Over 50 specimens fell within ASTM D4688’s unconditionally acceptable categories (i.e., Failure Modes 2 and 3）indicating high joint integrity. Additionally, the mean UTS values of all groups exceeded the UTS from SPS-1 and SPS-4 standards (18.1 MPa and 27.9 MPa, respectively). The 97 benchmark UTS of 136.0 MPa, based on Failure Modes 2 and 3, was even surpassed by DF lamellas with worn tools that failed in Modes 4 and 5. These results confirm that the FJ process used in this study produced strong, reliable joints well above industry expectations. The other significant outcome of this study is the complex role that tool condition played in determining FJ product quality. In this study, worn tools increased surface roughness parameters (SA, SP, and SV) of DF, which in turn promoted greater mechanical interlocking between wood surfaces and uncured adhesive in the early stage of adhesive application. This also resulted in a 20.5% increase in UTS for DF FJ lamellas, demonstrating that controlled tool wear could be beneficial to the desired level of surface roughness for acceptable UTS. In contrast, the excessive roughness measured in the lower-density WRC wood specimens combined with their higher extractives content -that reduced total surface energy and hindered adhesive spreading- led to lower bonding strength. Therefore, it is recommended that manufacturers adopt tool wear monitoring systems to maintain cutting tools within an optimal wear range, which differs amongst wood species. The goal would be to have a certain level of surface roughness that improves mechanical interlocking without compromising adhesive flow or chemical bonding performance. Across the three wood species studied, the change from sharp to worn cutting tools resulted in trends in surface roughness, IF-UTS, UTS and glue line delamination (Table 4.15). For DF wood, SA, IF-UTS, UTS, and delamination were all increased under worn tool conditions. This suggests that DF benefited from enhanced mechanical interlocking and improved adhesive penetration due to its overall higher density and more uniform surface roughness between early- and late-wood. In contrast, for HF wood, although roughness, IF-UTS, and delamination increased with worn tools, UTS decreased due to smoother surface which would yield thicker glueline and shallower glue penetration. A similar trend was observed for WRC, where roughness, IF-UTS, and delamination 98 also increased, but UTS decreased, likely due to interference from surface extractives, lower density and weaker structure. Although the increase in delamination was not statistically significant across tool and species conditions, these observable differences still highlighted the importance of surface quality in bond performance and the response of each species to machining parameters. Based on these findings, DF is recommended as the preferred species for high-performance FJ products, especially in operations where tool wear is difficult to control. HF requires machining precision to ensure surface smoothness, while WRC should be used cautiously and only when surface treatment methods are available to mitigate the effects of extractives. Table 4.15: Mean arithmetical mean deviation of the profile (SA), initial friction- ultimate tensile strength (IF-UTS), ultimate tensile strength (UTS) and delamination proportion of FJ lamellas made of Douglas-fir (DF), Hem-Fir (HF) and Western Redcedar (WRC) FJ with either freshly sharpened or worn cutting tools. Tool condition Wood species SA (μm) IF – UTS (MPa) UTS (MPa) Delamination (%) Processing recommendation Tool wear monitoring Recommendation Sharp DF 14.5 1.0 129.6 0.84 Recommended - HF 12.9 2.6 130.8 2.25 WRC 16.4 2.8 133.7 0.92 Worn DF 15.8 13.6 156.2 2.44 Recommended Tolerant to tool wear HF 14.0 11.9 116.9 2.69 Not recommended Less tolerant to tool wear WRC 16.8 10.2 118.6 2.11 Not recommended Furthermore, initial friction tests (both IF-UTS and IF-CoF) indicated that worn tools significantly improved pre-cure FJ integrity. Rougher surface increased the contact area and mechanical anchorage at the uncured adhesive interface, allowing the FJ lamellas to resist misalignment and separating during handling or transport before the adhesive completely curing. This suggests that, in high-speed production environments, a moderate increase in surface roughness can be achieved through controlled tool wear or light texturing which can enhance pre-cure stability without jeopardizing long-term bonding strength. 99 100 Chapter 5: Summary and conclusions 5.1 General conclusions This study aimed to evaluate the impact of tool wear levels on the surface quality and bonding performance of FJ lamellas made by DF, HF and WRC, focusing on their response to PUR adhesive bonding. The specific objectives of this research were to: (1) assess the effect of tool wear on surface quality (surface roughness, wettability, and microscopic characteristics); (2) quantify the influence of surface quality on the bonding strength of FJ lamellas using tensile tests and cyclic delamination tests; (3) correlate tool wear levels with bonding performance and determine the conditions that maximize bonding performance; and (4) quantify the initial friction properties (IF-UTS and IF-CoF) for varying tool wear levels and different species. For the first objective, the results clearly demonstrated that tool wear significantly increased surface roughness parameters (SA, SP, SV) across all three species. WRC exhibited the highest roughness values when machined with worn tools, followed by DF and HF. In addition to roughness, worn tools slightly reduced the total surface energy and its polar components, especially for HF and WRC, which likely limited adhesive spreading and wettability. Microscopic observations revealed that worn tools created more torn fibers and wave-like surface textures, potentially affecting adhesive interaction. For the second objective, the study found that there was noticeable impact of surface quality on bonding performance but vary by wood species. For DF, increased roughness from worn tools improved UTS but decreased in HF and WRC. Delamination proportions remained low across all species and tool conditions, with no statistically significant differences observed. However, a visible increase was noted under worn tool conditions. The flexible nature of PUR adhesive and 101 the controlled glueline thickness (below 0.3 mm) likely minimized the negative impact of surface variability on delamination performance. For the third objective, the findings indicate that the optimal bonding performance is species-specific. DF responded positively to moderate tool wear, showing improvement in both IF properties and UTS. In contrast, for HF and WRC, maintaining sharper tools is essential to preserve adhesive spreadability and avoid bond strength reduction. This demonstrates that achieving optimal surface roughness requires balancing tool wear to promote mechanical anchoring without compromising adhesive wettability, especially for species with lower density or higher extractive content like WRC. For the fourth objective, initial friction properties were significantly affected by tool wear. Worn tools led to substantial increases in IF-UTS and IF-CoF across all species where rougher surfaces contributed to better mechanical anchorage before adhesive curing. These findings highlight the critical role of surface roughness in ensuring pre-cure joint stability during handling and assembly in industrial production. 5.2 Limitations While this study provided valuable insights into the effects of tool wear and wood species on surface quality and bonding performance of FJ lamellas bonded with PUR adhesive, several limitations should be acknowledged. First, the tool wear levels investigated in this study were limited to two distinct conditions: freshly sharpened tools and worn tools after a predefined period of use. Although these conditions effectively represented typical industrial scenarios, the study did not include intermediate or progressive tool wear stages. As a result, the critical threshold of cutting edge rounding or recession that optimizes or deteriorates bonding performance could not be precisely identified. 102 Second, although the bonding performance was evaluated through tensile strength and cyclic delamination tests, the long-term durability of the bonded joints under real environmental conditions (e.g., multiple wet–dry cycles, temperature fluctuations, and outdoor exposure) was not fully assessed. The applied delamination test cycles followed standard protocols but may not capture the full extent of environmental stress experienced in-service. Finally, the experimental work was conducted under controlled laboratory and industrial machining settings using specific process parameters (feed speed, end pressure, adhesive type). Variability in industrial production lines, such as higher feed speeds, different adhesive formulations, or varying end pressures, could influence the applicability of the findings. 5.3 Recommendations for future research Building on the findings of this study, two areas of future research are proposed to deepen the understanding of machining effects and adhesive performance in finger-jointed wood products. 1. Studying a wider range of tool wear levels While this study examined two tool wear levels (sharp and worn), future work should explore a broader range of tool wear conditions, including precise measurements of cutting edge rounding and edge recession. This would help identify the critical thresholds at which surface roughness and mechanical properties (UTS and initial friction) are optimized or begin to degrade. 2. Evaluating the delamination after multiple wet-dry cycles The bond durability under extreme weathering should be further evaluated by subjecting specimens to extended wet–dry cyclic testing beyond the standard required, to better simulate long-term service conditions, particularly for exterior or load-bearing applications. Additionally, combining these extended durability tests with surface and subsurface analyses (e.g., micro-CT or 103 confocal microscopy) would provide insight into failure mechanisms. Together, these efforts would support the development of more predictive machining and bonding models, ultimately contributing to improved design and production strategies for engineered wood products. To ensure results are statistically relevant, multiple FJ from a single board could be used to assess various numbers of wet-dry cycles (e.g., 1, 2, 4 and 8). 104 Bibliography Abdel-Gadir, A. Y., Krahmer, R. L., & McKimmy, M. D. (1990). 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Purpose of machining Wood species FJ order (includes 13 samples) Max Amperage (kW) Pre-cut mean Amperage (kW) Post-cut mean Amperage (kW) Unglued DF 1 16.33 6.83 6.94 2 16.27 7.17 7.28 3 17.28 6.54 6.62 4 16.83 6.77 6.74 HF 1 16.2 6.76 6.77 2 15.43 6.70 6.86 3 16.3 6.59 6.62 4 13.31 6.71 6.67 WRC 1 13.75 6.69 7.29 2 16.19 6.64 7.11 3 15.99 6.55 6.77 4 16.65 6.59 6.71 Glued (Delamination) DF 1 17.56 6.92 6.91 2 17.39 6.45 6.64 HF 1 16.32 6.80 6.83 2 13.89 6.76 6.56 WRC 1 18.98 6.59 6.53 2 13.57 6.55 6.43 Glued (UTS) DF 1 16.76 6.42 6.47 2 17.58 6.70 6.76 3 16.4 6.36 6.58 4 17 7.11 6.58 HF 1 13.66 7.32 6.65 2 13.73 6.67 6.99 3 17.12 6.52 6.77 4 14.89 6.53 6.51 WRC 1 10.99 6.90 7.48 2 16.26 6.63 6.52 3 16.89 6.56 6.47 4 16.28 6.40 6.61 157 B.2 Power consumption during FJ manufacturing with worn tool Purpose of machining Wood species Finger-jointing order (includes 13 samples) Max Amperage (kW) Pre-cut mean Amperage (kW) Post-cut mean Amperage (kW) Unglued specimens DF 1 30.97 6.91 4.32 2 30.29 5.34 6.61 3 31.16 4.5 7.21 4 31.12 4.53 7.13 5 30.54 6.64 6.56 6 31.53 5.77 6.34 HF 1 28.97 6.01 6.9 2 29.27 5.47 6.33 3 30.33 4.46 4.79 4 30.2 7.03 6.36 5 30.21 4.92 6.6 6 29.99 5.9 6.58 WRC 1 30.41 5.15 7.33 2 30.98 6.5 6.17 3 30.18 5.01 4.48 4 30.53 5.01 6.18 5 30.44 7.26 5.93 6 30.81 4.84 6.74 Glued specimens (delamination) DF 1 30.75 4.62 6.43 2 30.96 6.75 6.98 3 29.9 6.32 4.98 4 29.75 5.67 4.42 5 28.75 5.02 5.17 6 30.36 6.13 6.23 HF 1 30.56 4.47 7.41 2 30.56 6.05 6.46 3 29.64 6.35 5.24 4 30.74 5.48 4.69 5 30.69 7.01 6.02 6 30.65 6.25 5.68 WRC 1 30.65 6.92 4.62 2 29.98 5.54 6.75 3 29.54 4.89 4.98 4 29.42 5.58 5.88 5 30.98 5.28 7.49 6 29.68 4.33 6.77 Glued specimens (UTS) DF 1 30.74 5.18 5.83 2 30.47 5.27 7.2 3 30.14 7.42 5.41 158 Purpose of machining Wood species Finger-jointing order (includes 13 samples) Max Amperage (kW) Pre-cut mean Amperage (kW) Post-cut mean Amperage (kW) Glued specimens (UTS) DF 4 29.2 4.41 6.76 5 29.1 6.94 6.21 6 29.37 5.26 7.1 HF 1 29.08 7.07 6.05 2 29.5 4.42 4.65 3 30.18 5.6 6.57 4 29.31 5.21 4.97 5 30.5 6.16 4.92 6 28.75 6.68 7.19 WRC 1 28.7 6.1 7.41 2 28.9 5.55 5.2 3 30.39 5.18 5.98 4 28.99 6.7 4.86 5 30.33 5.19 6.31 6 28.86 7.46 6.77
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Cool, Julie
Investigation of the effect of tool wear and wood species on surface quality and performance of finger-joint
Text