Batch Model Predictive Control for Selective Laser Melting

TL;DR

This paper introduces a Batch-Model Predictive Control method combining model predictive control and iterative learning control to improve temperature regulation in selective laser melting, enhancing part quality despite disturbances.

Contribution

It presents a novel control approach tailored for selective laser melting that effectively rejects disturbances and maintains desired thermal profiles.

Findings

Improved temperature tracking in simulations.

Effective disturbance rejection demonstrated.

Convergence to target temperature profile achieved.

Abstract

Selective laser melting is a promising additive manufacturing technology enabling the fabrication of highly customizable products. A major challenge in selective laser melting is ensuring the quality of produced parts, which is influenced greatly by the thermal history of printed layers. We propose a Batch-Model Predictive Control technique based on the combination of model predictive control and iterative learning control. This approach succeeds in rejecting both repetitive and non-repetitive disturbances and thus achieves improved tracking performance and process quality. In a simulation study, the selective laser melting dynamics is approximated with a reduced-order control-oriented linear model to ensure reasonable computational complexity. The proposed approach provides convergence to the desired temperature field profile despite model uncertainty and disturbances.