AdditiveGDL: Generative deep learning for predicting local thermal distributions in metal 3D-printed layers

In metal additive manufacturing, comprehending the intricate thermal dynamics during the printing process is paramount. These dynamics have far-reaching effects on various critical aspects including microstructure and mechanical properties, fatigue life, residual stresses, dimensional accuracy, shape integrity, and surface quality. Additionally, the scan strategy significantly impacts heat accumulation, especially at geometric features with sharp corners, overhangs, and thin walls. Thus, predicting heat distribution given the scan strategy becomes crucial. While numerical simulations using finite element methods are common, they can be computationally prohibitive for complex parts. In this paper, we propose a method that leverages constrained generative neural networks to predict the local thermal distribution given the laser toolpath. By identifying critical regions of heat accumulation, we can optimize geometry, and scan paths, ultimately enhancing the quality and reliability of 3D-printed metal components. Results show that generative deep learning offers an alternative approach to predicting the thermal field of printed layers efficiently.