Anomaly Detection of Laser Powder Bed Fusion Melt Pool Images Using Combined Unsupervised and Supervised Learning Methods

Abstract

Abstract Laser Powder Bed Fusion (LPBF) is one of the most promising forms of Additive Manufacturing (AM), allowing easily customized metal manufactured parts. Industry use is currently limited due to the often unknown and unreliable part quality, which is largely caused by the complex relationships between process parameters that include laser power, laser speed, scan strategy, and other machine settings. Melt pools can be monitored with a camera aligned co-axially with the laser to monitor part quality. However, the number of images acquired can be large, exceeding hundreds of thousands for a single part. This paper investigates how the K-Means algorithm, an unsupervised machine learning method, can be used to cluster images of melt pools based on their shape, including undesirable anomalous melt pools. Another unsupervised learning method in this paper is the U-Net autoencoder, which identifies anomalous melt pools by identifying images with a large reconstruction loss. The K-Means clustering or autoencoder provides labels that can be used for training a convolutional neural network image classifier. The image classifier can then be used to identify anomalous melt pools during the LPBF process. This paper provides a first step for real-time process control of the LPBF process by demonstrating how anomalous melt pools can be automatically identified in real-time.