Data-driven Linear Quadratic Tracking based Temperature Control of a Big Area Additive Manufacturing System

TL;DR

This paper develops and compares data-driven and model-based control strategies for temperature regulation in a large-scale additive manufacturing system, demonstrating effective control using only simulated data.

Contribution

It introduces a data-driven control approach for AM temperature management that matches traditional model-based methods, reducing the need for complex parameter estimation.

Findings

Data-driven control achieves performance parity with model-based methods.

Effective temperature control is possible using only simulated process data.

The approach advances towards autonomous manufacturing systems.

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 data-driven 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.