Detecting Porosity in Additively Manufactured Parts with Deep Learning and Artificial Computed Tomography Data

Deep learning has recently gained significant attention in nondestructive testing tasks due to its ability to effectively analyze complex data and identify patterns. This paper proposes a methodology for defect detection on an X-ray computed tomography dataset utilizing the Mask R-CNN algorithm. The proposed approach generates artificial data from the original dataset and introduces a metric for classifying the intensity of the defects. The Mask R-CNN is trained for defect localization and classification, and the results show that the proposed approach performs competitively with, and in many cases better than, reported papers on the same dataset. This paper provides a detailed analysis of the proposed methodology, including experimental results and discussions, demonstrating its effectiveness and potential for future research.