Leakage-Safe Machine Learning for Hydrogen Embrittlement Detection in 316L Stainless Steel: A Region-Held-Out Evaluation of Texture and Deep Features in SEM Micrographs
Advancing robust microstructure analysis through region-based validation to prevent AI overfitting in material science.
Scanning electron microscopy (SEM) is routinely used to characterize the microstructural changes caused by hydrogen embrittlement (HE) in structural steels. Machine learning can automate this characterization, but models are often evaluated using image-level splits. When several ...
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This research addresses the critical problem of data leakage in material science by implementing region-held-out evaluation techniques. By ensuring model generalization across spatial domains, it provides a more robust framework for detecting structural failure in high-stakes engineering environments.
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