机器学习驱动难熔高熵合金设计的现状与展望
Current status and prospects in machine learning-driven design for refractory high-entropy alloys
查看参考文献97篇
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文摘
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难熔高熵合金兼具高强度、高硬度、抗高温氧化等优异综合性能,在航空、航天、核能等领域具有广阔的应用前景和研究价值。但难熔高熵合金成分复杂、设计难度高,严重制约了高性能难熔高熵合金的进一步发展。近年来,机器学习凭借着高效准确的建模预测能力,逐步应用于高性能合金的设计和开发。本文在广泛收集机器学习驱动难熔高熵合金设计研究成果的基础上,详细综述了机器学习在辅助合金相结构设计、力学性能预测、强化机理分析和加速原子模拟等方面的应用与进展。最后,总结了该领域当前存在的不足,并针对如何推进高性能难熔高熵合金的设计进行了展望,包括构建难熔高熵合金高质量数据集、建立难熔高熵合金“成分-工艺-组织-性能”定量关系、实现高性能难熔高熵合金的多目标优化等。 |
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其他语种文摘
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Due to excellent comprehensive properties such as high strength, high hardness, and excellent high-temperature oxidation resistance, the refractory high-entropy alloys have broad application prospects and research value in the fields of aerospace and nuclear energy. However, the refractory high-entropy alloys have very complex composition features, making it difficult to perform alloy design. It seriously restricts the development of high-performance refractory high-entropy alloys. In recent years, the machine learning technique has been gradually applied to various high-performance alloys with efficient and accurate modeling and prediction capability. In this review, there was a comprehensive summary of research achievements on machine learning-driven design of refractory high-entropy alloys. A detailed review on the applications and progress of machine learning technique in different aspects was given, including alloy phase structure design, mechanical property prediction, strengthening mechanism analysis and acceleration of atomic simulations. Finally, the currently existing problems in this direction were summarized. The prospect about promoting the design of high-performance refractory high-entropy alloys was presented, including development of high-quality database for refractory high-entropy alloys, establishment of quantitative relation of “composition-process-structure-property” and achievement of multi-objective optimization of high-performance refractory high-entropy alloys. |
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来源
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材料工程
,2024,52(1):27-44 【核心库】
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DOI
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10.11868/j.issn.1001-4381.2023.000480
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关键词
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难熔高熵合金
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机器学习
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相结构
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力学性能
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强化机理
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原子模拟
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地址
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中南大学, 粉末冶金国家重点实验室, 长沙, 410083
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重庆大学, 国家镁合金材料工程技术研究中心, 重庆, 400044
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语种
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中文 |
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文献类型
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综述型 |
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ISSN
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1001-4381 |
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学科
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金属学与金属工艺 |
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基金
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国家重点研发计划项目
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湖南省杰出青年科学基金
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广西重点研发计划项目
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中南大学自主探索创新项目
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文献收藏号
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CSCD:7652189
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