Self-Supervised Crack Detection in X-Ray Computed Tomography Data of Additive Manufacturing Parts

Abstract

Following the current trends for minimizing human intervention in training intelligent architectures, this paper proposes a self-supervised method for quality control of Additive Manufacturing (AM) parts. An Inconel 939 sample is fabricated with the Laser Powder Bed Fusion (L-PBF) method and scanned using X-ray Computed Tomography (XCT) to reveal the internal cracks. A self-supervised approach was adopted by employing three modules that generate crack-like features for training a CycleGAN network. The proposed method generates random cracks based on a combination of uniform and normal random variables and outperforms the others in fine-grain crack detection and capturing narrow tips. A preliminary investigation of the training process shows that the algorithm has the capability of predicting the crack propagation direction as well.