On the application of machine learning for defect detection in L-PBF additive manufacturing

This paper investigates the performance of several Machine Learning (ML) techniques for online defect detection in the Laser Powder Bed Fusion (L- PBF) process. The research aims to improve the consistency in product quality and process reliability. The applications of acoustic emission (AE) sensor to receive elastic waves during the printing process is a cost-effective way of materializing such a demand. In this study, the process parameters were intentionally adjusted to create three different levels of defects in H13 tool steel samples. The first class was printed with minimum defects, the second class had only intentional cracks, and the last class included both intentional cracks and porosities. The AE signals were acquired during the samples' manufacturing, and three different machine learning (ML) techniques were applied to analyze and interpret the dataset. First, a hierarchical K-means clustering is employed for labeling the data, followed by a supervised deep learning neural network (DL) to match acoustic signal with defect type. Second, a principal component analysis (PCA) was used to reduce the dimensionality of the dataset. A Gaussian Mixture Model (GMM) was then employed to enable fast defect detection suitable for online monitoring. Third, a variational auto-encoder (VAE) approach is used to obtain a general feature of the signal that could be an input for the classifier. A supervised DL trained on the H13 tool steel dataset successfully detected quality trends in AE signals collected from 316L samples. The VAE approach presents a novel method to detect defects in L-PBF processes that eliminate the need for model training in different materials.