Explainability of Laser Powder Bed Fusion Melt Pool Classification Using Deep Learning

Abstract

Abstract Laser powder bed fusion (LPBF) has shown enormous potential for metal additive manufacturing in recent years. However, the relationship between the LPBF process parameters and part quality is not yet fully understood. Some LPBF machines now use cameras to monitor melt pools during manufacturing. Machine learning techniques have been proposed to analyze the melt pool data and to evaluate the quality of the manufacturing process. However, these machine learning techniques often appear as a black box and the underlying decisions made by the machine learning models are unknown. This paper proposes a neural network to classify the melt pool shapes using melt pool images and process parameters as model inputs. With both process parameters and the melt pool image being included, an explainable artificial intelligence (XAI) approach is developed to interpret the neural network and understand the relationships between the melt pool shape and the process parameters. Specifically, layer-wise relevance propagation (LRP) is used to reveal the relevance of process parameters in the neural network’s decision-making. Using LRP, relationships between the process parameters and melt pool shapes are revealed without explicit knowledge of the underlying physics. These relationships can potentially be used to adjust the process parameters and improve the quality of LPBF manufactured parts. This paper demonstrates how neural networks and XAI can effectively identify relationships between process parameters and LPBF melt pools.