Identification and Interpretation of Melt Pool Shapes in Laser Powder Bed Fusion with Machine Learning

Abstract

Abstract Laser powder bed fusion (LPBF) is a popular additive manufacturing process with many advantages compared with traditional (subtractive) manufacturing. However, ensuring the quality of LPBF parts remains a challenge in the manufacturing industry. This work proposes the use of unsupervised learning, specifically, the k-means clustering method, to identify unique melt pool shapes produced during LPBF manufacturing. Melt pools are a key process signature in LPBF and can assist in the evaluation of process quality. k-means is employed multiple times sequentially to produce clusters of melt pools, and the silhouette value is used to identify the optimal number of clusters. The clusters produced by k-means are used as labels to train a deep neural network to classify the melt pool shapes. By inputting the melt pool image and the corresponding LPBF machine process parameters into the neural network, the neural network identifies the melt pool shape to aid human analysis and provide insight into part quality. The trained neural network is interpreted using explainable artificial intelligence (XAI) methods to investigate the relationships between process parameters and the melt pool shape. Using layer-wise relevance propagation, the process parameters that most significantly influence the melt pool shapes are identified. The relationship between process parameters and melt pool shapes can be useful for selecting the process parameters to produce the desired melt pool shapes. In summary, this study describes an approach that combines unsupervised machine learning and XAI methods to effectively enable the analysis and interpretation of melt pools.