Extended reality-based training and adaptive machine learning-based optimization for thermoforming process
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Jalilvand, Iman
issued: 2025-01-16T23:57:33Z
issued: 2025
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Extended reality (XR) and machine learning (ML) are becoming pivotal enabling technologies in smart manufacturing, particularly for optimizing complex/multi-step processes. XR platforms provide immersive, interactive training environments for operators, while ML enables precise control and optimization of manufacturing processes. However, integrating these technologies into dynamic, real-time environments still presents challenges, particularly when faced with process variability and uncertainty. This dissertation addresses this technological trend in the thermoforming manufacturing process by developing a set of new XR systems, including virtual reality (VR) and mixed reality (MR) platforms integrated with real-time heat transfer simulations, for operator training. These immersive systems offer hands-on interaction with critical process variables such as temperature distribution, allowing operators to gain practical, real-world experience in a simulated environment. It is shown that the enhanced user engagement and precision achieved through the developed XR platforms significantly improve training performance metrics, as opposed to traditional training methods which often lack interactivity and real-time feedback. In the second part of this research, deep reinforcement learning (DRL) models are trained, using the same simulation models, and utilized to optimize the thermoforming process. Algorithms such as Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN) are employed and compared to control temperature distribution across thermoplastic sheet, resulting in better energy efficiency and product quality. The study also explores a multi-agent reinforcement learning (MARL) to optimize multi-heater systems in the process, providing a decentralized control and improving the model training process efficiency. Finally, a novel transfer learning (TL) application is introduced to improve the adaptability of the above RL models, ensuring a robust performance despit…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/90137
note: EXTENDED REALITY-BASED TRAINING AND ADAPTIVE MACHINE LEARNING-BASED OPTIMIZATION FOR THERMOFORMING PROCESS by Iman Jalilvand B.Sc., Science and Research Branch of Azad University, 2015 M.Sc., Amirkabir University of Technology, 2018 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The College of Graduate Studies (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) January 2025 © Iman Jalilvand, 2025 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: EXTENDED REALITY-BASED TRAINING AND ADAPTIVE MACHINE LEARNING-BASED OPTIMIZATION FOR THERMOFORMING PROCESS submitted by Iman Jalilvand in partial fulfilment of the requirements for the degree of Doctor of Philosophy Dr. Abbas S. Milani, School of Engineering Supervisor Dr. Bhushan Gopaluni, University of British Columbia Co-supervisor Dr. Rudolf Seethaler, School of Engineering Supervisory Committee Member Dr. Apurva Narayan, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Mohammad Khalad Hasan, Irving K. Barber Faculty of Science University Examiner Dr. Jinfeng Liu, University of Alberta External Examiner iii Abstract Extended reality (XR) and machine learning (ML) are becoming pivotal enabling technologies in smart manufacturing, particularly for optimizing complex/multi-step processes. XR platforms provide immersive, interactive training environments for operators, while ML enables precise control and optimization of manufacturing processes. However, integrating these technologies into dynamic, real-time environments still presents challenges, particularly when faced with process variability and uncertainty. This dissertation addresses this technological trend in the thermoforming manufacturing process by developing a set of new XR systems, including virtual reality (VR) and mixed reality (MR) platforms integrated with rea…
has type: Thesis/Dissertation
date issued: 2025-02
is shown at: 10.14288/1.0447767
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
contributor: Milani, Abbas S.
contributor: Gopaluni, Bhushan
type: Text
identifier URI: http://hdl.handle.net/2429/90137