NL-MambaXCT: Self-Supervised Nested-Learning Mamba for Nomex Honeycomb X-ray CT Defect Classification
Researchers have developed NL-MambaXCT, a novel framework utilizing Mamba architecture and self-supervised learning for defect classification in X-ray computed tomography (XCT) images of Nomex honeycomb structures. This approach combines masked image modeling for pre-training on unlabeled data with a Nested Learning formulation, featuring two-timescale parameter dynamics and a deep-momentum optimizer. The model achieved high accuracy and F1 scores, outperforming existing CNN, attention, and single-timescale Mamba baselines, suggesting its potential for efficient and robust industrial inspection in aerospace manufacturing. AI
IMPACT This research offers a more efficient and accurate method for defect detection in critical aerospace components, potentially improving manufacturing quality and safety.