Too hot to print, too slow to handle; finding optimal path characteristics for WAAM

Wire + Arc Additive Manufacturing (WAAM) is recognized as a highly capable metal additive process. However, the quality of manufactured parts is often compromised by defects stemming from process-induced temperature fields and transient weld-bead shapes, largely due to suboptimal path planning. Recent efforts have predominantly focused on optimizing either the thermal aspects or the productivity of WAAM, but the interplay between these factors and the final geometric accuracy of the parts has not been thoroughly investigated. This study introduces an automated framework for path planning optimization in WAAM, effectively bridging the gap between temperature control, geometric accuracy, and productivity. The approach utilizes a reinforcement learning agent to generate paths that minimize deposition time. These paths are subsequently segmented and optimized for their temperature response using a Monte Carlo tree search algorithm, with the thermal effects efficiently approximated through a reduced-order model. The paper presents a comparative analysis of various deposition strategies, offering insights and recommendations to enhance both productivity and part quality in WAAM path planning.