Prediction of melt pool width and layer height for Laser Directed Energy Deposition enabled by physics-driven temporal convolutional network

Abstract

Laser Directed Energy Deposition (L-DED) is a momentous metal additive manufacturing technology. Owing to high flexibility characteristic, it has been progressively adopted by high-value manufacturing industries. For the technology, one of the fundamental research challenges is how to accurately predict the melt pool size to ensure high-quality L-DED processes. To tackle the challenge, a novel physics-driven temporal convolutional network (TCN) approach is presented. In this research, the high prediction accuracy for L-DED is achievable via the following innovations: (i) a TCN model is designed as the core of the approach to leveraging the distinctive characteristics of the TCN model to address the temporal nature of the L-DED process during heat accumulation and incremental deposition; (ii) the physical models of the peak temperature, Marangoni effect and liquid jets affecting the melt pool formulization during the L-DED process are specified to strengthen the prediction accuracy of the approach. Experiments for manufacturing thin-walled parts using L-DED were conducted for approach validation and analyses. On average, the mean absolute percentage errors (MAPEs) of predicting the melt pool width and the layer height of a melt pool attained by this approach are 3.421% and 4.643%, respectively. The experiments demonstrate that the approach is competent to support the L-DED process in producing good-quality thin-walled parts.