Automated porosity assessment of parts produced by Laser Powder Bed Fusion using Convolutional Neural Networks

摘要: Laser Powder Bed Fusion (LPBF) is especially interesting for applications in industries with high quality requirements. There are different expensive and time-consuming strategies for quality assurance. A cheaper and faster approach is to analyze the data acquired during fabrication. In this work Convolutional Neural Networks (CNN) are investigated as a tool for data analysis of meltpool monitoring data. The goal is to automatically distinguish between porous and non-porous part regions. Therefore, the training data is categorized based on CT-scans of the test specimens. For increased interpretability of the results, Gradient-Weighted Class Activation Maps (Grad-CAM) are used.