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Feature Extraction and Microstructural Classification of Hot Stamping Ultra-High Strength Steel by Machine Learning

  • Machine Learning and New Paradigms in Computational Materials Research
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Abstract

Microstructural analysis and classification are important to understand the microstructure-property relationship of ultrahigh strength steel. Handling this manually takes time, and it requires years of training to reduce the number of errors. Automatic microstructure recognition is driven by machine learning and depends heavily on feature extraction from images at different phases. This paper compared two feature extraction methods, the gray-level cooccurrence matrix (GLCM) and convolutional neural networks (CNNs), to extract the features of different microstructures. The methods were applied to a database of etched and scanned electron microscopy (SEM)-imaged microstructures of ultrahigh strength steel. Driven by metallographic knowledge, each characteristic parameter of the GLCM was analyzed. Then, the t-distributed stochastic neighbor embedding (t-SNE) method was used to compare the two high-dimensional features, and the results showed that feature clustering of the CNN method was better than the GLCM. Subsequently, the features of microstructures were classified by machine learning (ML), and the results showed that the features from CNNs had a better recognition accuracy (98.50%) than those from support vector machine (SVM).

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Acknowledgements

This research work was financially supported by the National Natural Science Foundation of China (Grant No. U1760205) and the National Science and Technology Major Project of China (Grant No. 2018ZX04023001).

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Authors and Affiliations

  1. School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, People’s Republic of China

    Bin Zhu, Zhexing Chen, Fangkang Hu, Liang Wang & Yisheng Zhang

  2. Institute of Aerospace Science and Technology, Wuhan University, Wuhan, 430072, People’s Republic of China

    Xiaobin Dai

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  1. Bin Zhu
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  2. Zhexing Chen
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  3. Fangkang Hu
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  4. Xiaobin Dai
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Correspondence to Liang Wang.

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Zhu, B., Chen, Z., Hu, F. et al. Feature Extraction and Microstructural Classification of Hot Stamping Ultra-High Strength Steel by Machine Learning. JOM 74, 3466–3477 (2022). https://doi.org/10.1007/s11837-022-05265-5

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  • DOI: https://doi.org/10.1007/s11837-022-05265-5

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