Layer-Wise Emission Prediction for Laser Powder Bed Fusion With an Attribution-Based Explainable Deep Learning Model

Abstract While the complexity of laser powder bed fusion (LPBF) processes facilitates customized and metal-based functional parts to be built, existing process monitoring techniques have limitations. Therefore, the need for intricate process monitoring has grown. Non-uniform emission readings are correlated with overheating. Therefore, process monitoring of areas experiencing excess thermal emission during print to track potential overheating is needed. A process monitoring technique using deep neural network-long short-term memory (DNN-LSTM) deep learning (DL) models for emission tracking has been developed. The DNN component harnesses process parameters, while the LSTM harnesses the time-series emission structure on multiple sets of prints in parallel. Moreover, trust and interpretation of the opaque methodology are needed to make the process widely applicable. Existing explainable artificial intelligence (XAI) methods are inoperative with the model developed. We overcome this gap by developing an attribution-based XAI-enabled DNN-LSTM for predicting, explaining, and evaluating layer-wise emission prediction. Interpretation from attribution-based methods, namely, Shapley additive explanations, integrated gradient explanations, and local interpretable model-agnostic explanations, reveal an estimate of how each physics variable (process parameters, layer number, layer-wise average emission readings) impacts each future layer-wise average emission behavior as decided by the DL model. Finally, existing evaluation metrics of XAI are mostly domain-focused. We overcome this gap by establishing evaluation criteria appropriate for understanding the trust of the explanations in the context of thermal emission prediction for LPBF.