吴铮, 李全安, 陈晓亚, 郑泽宇, 张娜娜, 王政. 机器学习在镁合金应用中的研究进展[J]. 工程科学学报, 2024, 46(10): 1797-1811. DOI: 10.13374/j.issn2095-9389.2024.03.10.002
引用本文: 吴铮, 李全安, 陈晓亚, 郑泽宇, 张娜娜, 王政. 机器学习在镁合金应用中的研究进展[J]. 工程科学学报, 2024, 46(10): 1797-1811. DOI: 10.13374/j.issn2095-9389.2024.03.10.002
WU Zheng, LI Quanan, CHEN Xiaoya, ZHENG Zeyu, ZHANG Nana, WANG Zheng. Applications of machine learning on magnesium alloys[J]. Chinese Journal of Engineering, 2024, 46(10): 1797-1811. DOI: 10.13374/j.issn2095-9389.2024.03.10.002
Citation: WU Zheng, LI Quanan, CHEN Xiaoya, ZHENG Zeyu, ZHANG Nana, WANG Zheng. Applications of machine learning on magnesium alloys[J]. Chinese Journal of Engineering, 2024, 46(10): 1797-1811. DOI: 10.13374/j.issn2095-9389.2024.03.10.002

机器学习在镁合金应用中的研究进展

Applications of machine learning on magnesium alloys

  • 摘要: 在材料基因工程的背景下,基于数据驱动的机器学习技术作为一种强大的新型工具在镁合金的研究领域得到了广泛的关注. 机器学习可以绕过几乎任何复杂的实验过程,只要确定描述符和目标属性之间的联系,就能以较低的成本,快捷地预测材料的性能. 传统的实验试错法和基于密度泛函理论的方法由于时间成本高、效率低,难以满足材料科学的不断发展需求. 本文综述了机器学习在镁合金应用中的研究进展. 首先简述了机器学习的基本流程和各种方法,主要包括数据集收集、数据预处理、模型构建和性能评估,并对机器学习算法的分类进行了总结. 重点介绍了机器学习在镁合金加工工艺、显微组织、力学性能、耐蚀性能、储氢性能、固有属性(强化机制、各向异性等)和逆向设计等诸多方面应用的研究成就. 机器学习模型不仅加速了新型高性能镁合金的设计过程,而且推动了镁合金塑性变形机理的深入研究. 最后,分析了机器学习在镁合金研究应用中一些亟待解决的问题,并据此提出了机器学习在镁合金应用方面未来的研究方向和发展趋势.

     

    Abstract: In materials genetic engineering, data-driven machine learning techniques have garnered significant attention as a powerful new tool in the field of magnesium alloys. Traditional empirical trial-and-error methods and those based on density functional theory have struggled to keep pace with the continuous advancements in material science needs owing to high time costs and low efficiency. By relying on statistical methods instead of solving physical equations, machine learning can quickly predict material properties at a low cost, provided the connection between descriptors and target properties is identified. This capability can streamline the experimental process. Magnesium and its alloys show tremendous potential in aerospace, automotive, and other fields owing to their low density and high specific strength. However, their industrialization has been limited by several challenges, including the varied effects of different alloying elements, preparation and processing defects, deformation difficulties, and the common trade-off between strength and ductility. Machine learning can accelerate the discovery of novel magnesium alloys or processing parameters, and explore the relationships between their physicochemical characteristics and target properties. This paper comprehensively and systematically reviews the research progress of machine learning applications in magnesium alloys. It introduces the basic processes and various methods of machine learning, including data set collection, data preprocessing, model building, and performance evaluation. The classification of machine learning algorithms is summarized briefly. The paper then focuses on the research achievements of machine learning applied in many aspects, such as machining processes, microstructure, mechanical properties, corrosion resistance, hydrogen storage properties, intrinsic properties (reinforcement mechanism, anisotropy, etc.) and inverse design. Factors such as alloy compositions, test temperature and time, second phase, and Schmid factor can be considered as features and input into machine learning models for training. These models not only accelerate the design of novel high-performance magnesium alloys but also enhance the understanding of magnesium alloy mechanisms. Additionally, the paper analyzes some urgent issues in the research and application of machine learning in magnesium alloys. These include insufficient prediction of the chemical and physical properties of magnesium alloys, the nascent stage of predicting the design and service performance of magnesium alloy components, and the lack of high-quality data sets. Finally, the paper proposes future research directions and development trends in the application of machine learning in magnesium alloys.

     

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