Geometry-agnostic data-driven thermal modeling of additive manufacturing processes using graph neural networks

Abstract

Additive manufacturing (AM) is commonly used to produce builds with complex geometries. Despite recent advancements in data-driven modeling of AM processes, the generalizability of such models across a wide range of geometries has remained a challenge. Here, a graph-based representation using neural networks is proposed to capture spatiotemporal dependencies of thermal responses in AM processes. Our results tested on the Directed Energy Deposition process, indicate that our deep learning architecture accurately predicts long thermal histories for unseen geometries in the training process, offering a viable alternative to expensive computational mechanics or experimental solutions.