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Volume 30 Issue 6
Jun.  2023
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文章导航 >  矿物冶金与材料学报（英文）  > 2023  >  30(6): 1003-1024
Guangfei Pan, Feiyang Wang, Chunlei Shang, Honghui Wu, Guilin Wu, Junheng Gao, Shuize Wang, Zhijun Gao, Xiaoye Zhou,  and Xinping Mao, Advances in machine learning- and artificial intelligence-assisted material design of steels, Int. J. Miner. Metall. Mater., 30(2023), No. 6, pp. 1003-1024. https://doi.org/10.1007/s12613-022-2595-0
Cite this article as:
Guangfei Pan, Feiyang Wang, Chunlei Shang, Honghui Wu, Guilin Wu, Junheng Gao, Shuize Wang, Zhijun Gao, Xiaoye Zhou,  and Xinping Mao, Advances in machine learning- and artificial intelligence-assisted material design of steels, Int. J. Miner. Metall. Mater., 30(2023), No. 6, pp. 1003-1024. https://doi.org/10.1007/s12613-022-2595-0
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特约综述

机器学习和人工智能辅助钢铁材料设计的研究进展

  • 1)

    北京科技大学材料基因组工程北京市高精尖创新中心, 北京 100083, 中国

  • 2)

    广东省材料科学与技术实验室阳江分部(阳江先进合金实验室), 阳江 529500, 中国

  • 3)

    广东省海洋土木工程耐久性重点实验室, 深圳大学土木工程学院, 深圳 518060, 中国

  • 通讯作者:

    吴宏辉    E-mail: wuhonghui@ustb.edu.cn

    汪水泽    E-mail: wangshuize@ustb.edu.cn

文章亮点

  • (1) 以材料四面体为导向，系统地综述了机器学习方法在钢铁材料“成分-工艺-组织-性能”研究领域的应用。
  • (2) 机器学习算法的快速发展将显著提升对结构材料构效关系的深入理解。
  • (3) 基于计算材料学、迁移学习和数据挖掘等方法以扩展数据集，是未来机器学习和人工智能辅助钢材设计的重要发展方向。
  • 随着人工智能技术的快速发展和材料数据的显著增加，机器学习和人工智能辅助设计高性能钢材正成为材料科学的主流范式。机器学习方法是一种基于计算机科学、统计学及材料科学之间的跨学科科学，聚焦于发现众多数据之间的相关性。与材料科学中传统的物理建模方法相比，机器学习方法的主要优势在于克服了材料本身复杂的物理机制，为新型高性能材料的研发提供了新的思路。本文从数据预处理和机器学习模型的介绍开始，包括算法选择和模型评估。然后，以优化成分、结构、工艺和性能为主题，回顾了机器学习方法在钢铁研究领域应用的一些典型案例。此外，还介绍了机器学习方法在以性能为导向的材料成分逆向设计工程以及在钢材缺陷检测领域中的应用。最后，探讨了机器学习在材料领域的适用性和局限性，并对未来的发展方向和前景进行了展望。
    • Invited Review

    Advances in machine learning- and artificial intelligence-assisted material design of steels

    + Author Affiliations
      Abstract
    • With the rapid development of artificial intelligence technology and increasing material data, machine learning- and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials science. Machine learning methods, based on an interdisciplinary discipline between computer science, statistics and material science, are good at discovering correlations between numerous data points. Compared with the traditional physical modeling method in material science, the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials. This review starts with data preprocessing and the introduction of different machine learning models, including algorithm selection and model evaluation. Then, some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition, structure, processing, and performance. The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed. Finally, the applicability and limitations of machine learning in the material field are summarized, and future directions and prospects are discussed.
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