Zavrakli, Eleni (2023) Reinforcement Learning and Optimal Control for Additive Manufacturing. PhD thesis, National University of Ireland Maynooth.

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Abstract

Designing efficient closed-loop control algorithms is a key issue in Additive Manufacturing (AM), as various aspects of the AM process require continuous monitoring and regulation, with temperature being a particularly significant factor. Here we study closed-loop control of a state space temperature model with a focus on both model-based and datadriven methods. We demonstrate these approaches using a simulator of the temperature evolution in the extruder of a Big Area Additive Manufacturing system (BAAM). We perform an in-depth comparison of the performance of these methods using the simulator. We find that we can learn an effective controller using solely simulated process data. Our approach achieves parity in performance compared to model-based controllers and so lessens the need for estimating a large number of parameters of the intricate and complicated process model. We believe this result is an important step towards autonomous intelligent manufacturing.

Item Type:	Thesis (PhD)
Keywords:	Reinforcement Learning; Optimal Control; Additive Manufacturing;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	18850
Depositing User:	IR eTheses
Date Deposited:	10 Sep 2024 14:37
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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