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<p>The evolution of microstructure during hot-rolling production constitutes a pivotal factor in determining the quality and properties of steel. However, the unique characteristic of steel materials lies in their intrinsic microstructural sensitivity to composition and processing conditions. Therefore, precisely establishing quantitative relationships between composition, processing, structure, and properties (CPSP) in the hot-rolling production process of steel, and thoroughly investigating the key characteristic parameters influencing performance, are of paramount importance for enhancing the properties of steel products. Based on this, this paper proposes a multimodal data fusion approach. By mining multi-source information, such as microstructural images, composition, and processing conditions, to capture richer features, it employs Gradient-weighted Class Activation Mapping (Grad-CAM) to visually analyze the correlation between microstructural characteristics (size, phase composition, etc.) and yield strength. The results demonstrate that this method achieves significantly higher computational accuracy than purely numerical models, while also revealing the crucial mechanism by which the bainite phase and its fine grains influence yield strength. This provides novel insights for accurately and comprehensively elucidating the complex nonlinear relationships between composition, microstructure, processing, and properties.</p>

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Quantitative relationship mining of steel CPSP through multimodal data fusion

  • 期刊论文

  • 发表日期：

    2026年5月31日

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  • Chengde Zhang,
  • Guangming Cao,
  • Siwei Wu,
  • Xinyue Yan,
  • Weiliang Xiong,
  • Hongbin Wang,
  • Ning Liu,
  • Zhenyu Liu

摘要

The evolution of microstructure during hot-rolling production constitutes a pivotal factor in determining the quality and properties of steel. However, the unique characteristic of steel materials lies in their intrinsic microstructural sensitivity to composition and processing conditions. Therefore, precisely establishing quantitative relationships between composition, processing, structure, and properties (CPSP) in the hot-rolling production process of steel, and thoroughly investigating the key characteristic parameters influencing performance, are of paramount importance for enhancing the properties of steel products. Based on this, this paper proposes a multimodal data fusion approach. By mining multi-source information, such as microstructural images, composition, and processing conditions, to capture richer features, it employs Gradient-weighted Class Activation Mapping (Grad-CAM) to visually analyze the correlation between microstructural characteristics (size, phase composition, etc.) and yield strength. The results demonstrate that this method achieves significantly higher computational accuracy than purely numerical models, while also revealing the crucial mechanism by which the bainite phase and its fine grains influence yield strength. This provides novel insights for accurately and comprehensively elucidating the complex nonlinear relationships between composition, microstructure, processing, and properties.