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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. 

Physics-informed machine learning is emerging through vast methodologies and in various applications. This paper discovers physics-based custom loss functions as an implementable solution to additive manufacturing (AM). Specifically, laser metal deposition (LMD) is an AM process where a laser beam melts deposited powder, and the dissolved particles fuse to produce metal components. Porosity, or small cavities that form in this printed structure, is generally considered one of the most destructive defects in metal AM. Traditionally, computer tomography scans measure porosity. While this is useful for understanding the nature of pore formation and its characteristics, purely physics-driven models lack real-time prediction ability. Meanwhile, a purely deep learning approach to porosity prediction leaves valuable physics knowledge behind. In this paper, a hybrid model that uses both empirical and simulated LMD data is created to show how various physics-informed loss functions impact the accuracy, precision, and recall of a baseline deep learning model for porosity prediction. In particular, some versions of the physics-informed model can improve the precision of the baseline deep learning-only model (albeit at the expense of overall accuracy).