Output Feedback Reinforcement Learning with Parameter Optimisation for Temperature Control in a Material Extrusion Additive Manufacturing system

TL;DR

This paper develops reinforcement learning controllers for temperature regulation in additive manufacturing, enabling optimal control without detailed process models, thus advancing autonomous manufacturing capabilities.

Contribution

It introduces a model-free reinforcement learning approach for temperature control in additive manufacturing, removing the need for precise system modeling.

Findings

Model-free RL achieves comparable control performance to model-based methods.

The approach demonstrates effective temperature regulation in a simulated BAAM system.

Results support autonomous, intelligent manufacturing with minimal process modeling.

Abstract

With the rapid development of Additive Manufacturing (AM) comes an urgent need for advanced monitoring and control of the process. Many aspects of the AM process play a significant role in the efficiency, accuracy and repeatability of the process, with temperature regulation being one of the most important ones. In this work, we solve the problem of optimal tracking control for a state space temperature model of a Big Area Additive Manufacturing (BAAM) system. In particular, we address the problem of designing a Linear Quadratic Tracking (LQT) controller when access to the exact system state is not possible, except in the form of measurements. We initially solve the problem with a model-based approach based on reinforcement learning concepts, with state estimation through an observer. We then design a model-free reinforcement-learning based controller with an internal state estimation step and demonstrate its performance through a simulator of the systems' behaviour. Our results showcase the possibility of achieving comparable results while learning optimal policies directly from process data, without the need for an accurate, intricate model of the process. We consider this outcome to be a significant stride towards autonomous intelligent manufacturing.