AI-Powered Multi-Class Defect Segmentation in Industrial CT Data

Abstract

Industrial computed tomography is a key technology for non-destructive testing for many applications. The growing demand of automated testing requires an efficient automated data analysis. In this work we propose a solution using a neural network for voxel-accurate segmentation of 4 relevant defect classes in industrial casting parts and additively manufactured parts. Defect classes are pores, cracks, inclusions and pore nests. The network was trained and evaluated with a dataset of 20,000 samples. We used a modified U-Net architecture originally developed for medical image segmentation which we optimized for our industrial CT-Data and the multi-class segmentation task. In this work we show that we were able to achieve promising results of ~82 % MeanIoU score and individual defect class MeanIoU values of up to 95 % segmentation performance and qualitatively a high classification performance.