Physics-based simulation and reinforcement learning control of the heating phase in the thermoforming process
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Hosseinionari, Hadi
issued: 2024-08-01T16:34:11Z
issued: 2024
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: The importance of thermoforming in manufacturing has increased due to the growing demand for plastic products across various industries. The large-scale production of thermoformed parts has necessitated the development of precise and cost-effective process controllers. The thermoforming industry lags behind manufacturing processes, like injection molding, in terms of automation, control, and process understanding. As such, the thermoforming industry is currently facing the critical task of increasing its degree of process control. The thermoforming process consists of two primary phases: the heating phase and the vacuum and cooling phase. The heating phase stands out as the most critical step, as the outcomes of the subsequent phase hinge on its effectiveness. Despite the development of control techniques for the heating phase of the process, oven heater temperatures in the thermoforming industry are still mostly regulated by experienced operators using trial and error. The designed control algorithm for the heating phase in thermoforming must be easy to implement, cost-effective, robust to environmental disturbances, and should minimize control time to maximize production efficiency. To bridge these existing gaps, the first objective of this thesis is to develop a precise and computationally efficient model for the heating phase in thermoforming. This 2D model, by utilizing the experimental radiation pattern, achieves a level of accuracy comparable to a 3D model (decreasing the MSE by four-fold compared to traditional 2D models) while maintaining the computational efficiency characteristic of a 2D model (ten times faster than 3D models). The complexity of this model makes it difficult for traditional controller development. Thus, it is used to collect data for data-driven methods or to train various reinforcement learning (RL) control approaches. The controller is strategically designed and trained based on the system’s input and output instead of facing the comple…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/88829
note: Physics-based Simulation andReinforcement Learning Control of theHeating Phase in the ThermoformingProcessbyHadi HosseinionariB.Sc., The University of Mohaghegh Ardabili, 2011M.Sc., K. N. Toosi University of Technology, 2013A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe College of Graduate Studies(Mechanical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)July 2024© Hadi Hosseinionari, 2024The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a dissertation entitled:Physics-based Simulation and Reinforcement Learning Con-trol of the Heating Phase in the Thermoforming Processsubmitted by Hadi Hosseinionari in partial fulfilment of the requirementsof the degree of Doctor of PhilosophyDr. Rudolf Seethaler, School of Engineering.SupervisorDr. Abbas S. Milani, School of Engineering.Supervisory Committee MemberDr. Bhushan Gopaluni, The University of British Columbia (Vancouver).Supervisory Committee MemberDr. Ian Foulds, School of Engineering.University ExaminerDr. Syed Imtiaz, Memorial University of Newfoundland.External ExamineriiAbstractThe importance of thermoforming in manufacturing has increased dueto the growing demand for plastic products across various industries. Thelarge-scale production of thermoformed parts has necessitated the develop-ment of precise and cost-effective process controllers. The thermoforming in-dustry lags behind manufacturing processes, like injection molding, in termsof automation, control, and process understanding. As such, the thermo-forming industry is currently facing the critical task of increasing its degreeof process control.The thermoforming process consists of two primary phases: the heatingphase and the vacuum and cooling phase. The heating phase stands out asthe most critical step, as the outcomes of the subsequent phase hinge on itseffectiveness. Despite the development of control techniques for the …
has type: Thesis/Dissertation
date issued: 2024-09
is shown at: 10.14288/1.0444963
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: Seethaler, Rudolf
type: Text
identifier URI: http://hdl.handle.net/2429/88829