Powder Bed Defect Extraction of Laser Powder Bed Fusion Additive Manufacturing with Tensor Robust Principal Component Analysis

The process monitoring and quality control of metal additive manufacturing has been a research hotspot in recent years. AI-driven laser powder bed fusion process monitoring is currently the most popular research idea. However, machine learning methods have high requirements for data sets and require a lot of cost. Therefore, this paper attempts to use a new technology, namely tensor robust principal component analysis, to directly analyze and process powder bed images, to realize the extraction of powder bed defects. The main steps include: 1) synthesizing a large number of powder bed images into a tensor; 2) separating the tensor into low-rank components and sparse components. By analyzing and processing the powder bed images collected during the two printing processes and the powder bed images with serious defects collected during a long period, the problems and suitable use scenarios of tensor robust principal component analysis in dealing with powder bed defects are discussed. It is found that it has the best effect in dealing with continuous powder bed images with variable defects.