Tool wear prediction and monitoring in edge trimming of carbon fiber reinforced polymer using machine learning methods
department or school: Applied Science, Faculty of
department or school: Mechanical Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Jin, Fan
issued: 2021-04-30T21:41:42Z
issued: 2021
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Edge trimming is a necessary machining operation to achieve the required dimension and surface quality of carbon fiber reinforced polymer (CFRP) components. Rapid tool wear occurs during the edge trimming process owing to the high strength and abrasive nature of the carbon fibers, leading to potential surface damage. To satisfy the production efficiency and part quality requirements, prediction and monitoring of tool wear progression are essential in edge trimming of CFRP. This thesis presents the tool wear prediction and monitoring in edge trimming of unidirectional (UD) and multidirectional (MD) CFRPs using the artificial neural network (ANN) and random forest (RF) algorithms. The objective is to achieve the tool wear prediction and monitoring under various process conditions with limited training data. For edge trimming of UD CFRP, the feature parameters of the machine learning models consist of the instantaneous process parameters, such as the instantaneous radial force, fiber cutting angle, and uncut chip thickness. As a result, the training data obtained at one radial and axial depth of cut are applicable for tool wear prediction at other radial cutting depths. The experimental data shows a high correlation between the instantaneous radial force and the tool flank wear length. The proposed method achieves the tool wear length prediction within 10% error under various edge trimming conditions. The experimental data from UD CFRP is used for tool wear monitoring in edge trimming of MD CFRP. The tool wear length ratios among the tool edge portions corresponding to different UD layers of the MD CFRP are experimentally identified. The overall radial force is predicted by individual force components from each UD layer, which are influenced by the cutting speed, feed rate, tool helix angle, and wear length distribution along the tool edge. The results show that the proposed model predicts the radial force in edge trimming of MD CFRP with different layer-up sequences w…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/78057
note: Tool Wear Prediction and Monitoring in Edge Trimming of Carbon Fiber Reinforced Polymer Using Machine Learning Methods by Fan Jin B.Eng., Tianjin University, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2021 © Fan Jin, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Tool Wear Prediction and Monitoring in Edge Trimming of Carbon Fiber Reinforced Polymer Using Machine Learning Methods submitted by Fan Jin in partial fulfillment of the requirements for the degree of Master of Applied Science in Mechanical Engineering Examining Committee: Dr. Xiaoliang Jin, Assistant Professor, Department of Mechanical Engineering, UBC Supervisor Dr. Yusuf Altintas, Professor, Department of Mechanical Engineering, UBC Supervisory Committee Member Dr. Jasmin Jelovica, Assistant Professor, Department of Civil Engineering, UBC Supervisory Committee Member iii Abstract Edge trimming is a necessary machining operation to achieve the required dimension and surface quality of carbon fiber reinforced polymer (CFRP) components. Rapid tool wear occurs during the edge trimming process owing to the high strength and abrasive nature of the carbon fibers, leading to potential surface damage. To satisfy the production efficiency and part quality requirements, prediction and monitoring of tool wear progression are essential in edge trimming of CFRP. This thesis presents the tool wear prediction and monitoring in edge trimming of unidirectional (UD) and multidirectional (MD) CFRPs using the artificial neural network (ANN) and random forest (RF) algorithms. The objective is to achieve the tool wear prediction and monitoring under various process conditions with limited training data. For edge tri…
has type: Thesis/Dissertation
date issued: 2021-11
is shown at: 10.14288/1.0397112
language: eng
degree discipline: Mechanical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/78057