A Model-Based Reinforcement Learning and Correction Framework for Process Control of Robotic Wire Arc Additive Manufacturing

Abstract

Robotic Wire Arc Additive Manufacturing (WAAM) utilizes a robot arm as a motion system to build 3D metallic objects by depositing weld beads one above the other in a layer by layer fashion. A key part of this approach is the process study and control of Multi-Layer Multi-Bead (MLMB) deposition, which is very sensitive to process parameters and prone to error stacking. Despite its importance, it has been receiving less attention than its single bead counterpart in literature, probably due to the higher experimental overhead and complexity of modeling. To address these challenges, this paper proposes an integrated learning-correction framework, adapted from Model-Based Reinforcement Learning, to iteratively learn the direct effect of process parameters on MLMB print while simultaneously correct for any inter-layer geometric digression such that the final output is still satisfactory. The advantage is that this learning architecture can be used in conjunction with actual parts printing (hence, in-situ study), thus minimizing the required training time and material wastage. The proposed learning framework is implemented on an actual robotic WAAM system and experimentally evaluated.