Machine learning of automatic hierarchical multi-label classification method for identifying metal failure mechanisms

Machine learning of automatic hierarchical multi-label classification method for identifying metal failure mechanisms

Abstract In this study, a hierarchical multi-label classification method called HFFNet-2d is proposed for the automatic classification of scanning electron microscope (SEM) images of metal failure. The method combines the advantages of convolutional neural networks (CNN) and Vision Transformers (ViT...

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Bibliographic Details
Main Authors: Ruitong Han, Chang-Bo Liu, Wanting Sun, Shuai Yu, Haoran Zheng, Lin Deng
Format: Article
Language:English
Published: Nature Portfolio 2025-06-01
Series:Scientific Reports
Subjects:
Scanning Electron microscope
Metal failure analysis
Deep learning
GradCAM
Convolutional neural networks
Vision Transformers
Online Access:https://doi.org/10.1038/s41598-025-05076-z
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