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材料导报  2026, Vol. 40 Issue (17): 25060082-11    https://doi.org/10.11896/cldb.25060082
  金属与金属基复合材料 |
颗粒增强铝基复合材料力学性能影响因素及其性能预测模型发展趋势
赵栋才1,*, 白雪锋1, 冷金凤2, 孙吉1, 郑军1,*
1 安徽工业大学先进金属材料绿色制备与表面技术教育部重点实验室,安徽 马鞍山 243002
2 南京工业大学先进轻质高性能材料研究中心,南京 210009
Influencing Factors on the Mechanical Properties of Particle-reinforced
Aluminum Matrix Composites and Development Trends of Their
Performance Prediction Models
ZHAO Dongcai1,*, BAI Xuefeng1, LENG Jinfeng2, SUN Ji1, ZHENG Jun1,*
1 Key Laboratory of Green Fabrication and Surface Technology of Advanced Metal Materials, Ministry of Education, Anhui University of Technology, Maanshan 243002, Anhui, China
2 Key Laboratory for Light-weight Materials, Nanjing Tech University, Nanjing 210009, China
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摘要 颗粒增强铝基复合材料具有高比强度、良好的热稳定性和高耐磨性等优点,受到越来越多的重视,但其研究内容非常驳杂。通过文献综述发现,影响其屈服强度、抗拉强度、伸长率和硬度的关键因素不仅包括颗粒增强相的种类、大小、空间分布和体积分数,还包括制备技术,在力学性能预测方面,需根据具体情况选择不同的增强理论模型。目前的增强机制模型仍然以50多年前提出的载荷传递效应、位错强化和基于塑性变形的强化机制为基础,但其组合更为繁杂,即依据微观结构的观察结果进行增强机制的筛选和组合,完成计算模型的构建。这意味着虽然研究越来越精细,但这些组合很难推广至具体的材料制备工艺范围之外;另一方面,大量数据的积累为深度学习提供了可能。深度学习跳过了关于增强机制的组合,能根据大量的数据提供深刻洞见,跨越不同工艺之间的障碍,具有发展出普适性指导价值模型的潜力。当下开展基于微观结构的模型设计和训练是极具价值的研究方向。
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赵栋才
白雪锋
冷金凤
孙吉
郑军
关键词:  颗粒增强铝基复合材料  力学性能  增强机理  深度学习    
Abstract: Particle-reinforced aluminum matrix composites (PRAMCs), known for their high specific strength, thermal stability, and excellent wear resis-tance, have garnered increasing attention, but their research content is highly diverse and complex. Through a literature review, it has been found that the key factors that influence their yield strength, tensile strength, elongation, and hardness include not only the type, size, spatial distribution, and volume fraction of the particle-reinforcing phase but also the preparation techniques. For predicting mechanical properties, different reinforcing theoretical models should be selected based on specific circumstances. Current reinforcing mechanism models are still primarily based on the load transfer effect, dislocation strengthening, and strengthening mechanisms based on plastic deformation, which were proposed over five decades ago. At the same time, through the selection and integration of reinforcing mechanisms based on microscopic structural observations to construct computational models, their combinations are extremely intricate. These indicate that although research is increasingly refined, the combinations are challenging to generalize beyond specific material preparation processes. Nevertheless, the accumulation of vast amounts of data has made deep learning feasible. Deep learning bypasses the need to combine reinforcing mechanisms and can provide profound insights based on extensive data, overcoming barriers between different processes and holding the potential to develop universally applicable guiding models. Currently, conducting model design and training based on microscopic structures represents a highly valuable research direction.
Key words:  particle-reinforced aluminum matrix composite    mechanical property    reinforcement mechanism    deep learning
出版日期:  2026-09-10      发布日期:  2026-09-22
ZTFLH:  TB331  
基金资助: 国家重点研发计划(2024YFB3714100)
通讯作者:  *赵栋才,硕士,教授,主要从事复合材料制备技术、复合材料表面多功能一体化涂层、耐磨减摩涂层和物理气相沉积装备的研究。dczhao@ahut.edu.cn;郑军,博士,教授,博士研究生导师,主要从事金属与非金属材料表面防护及其工程应用研究。Jzheng@ahut.edu.cn   
引用本文:    
赵栋才, 白雪锋, 冷金凤, 孙吉, 郑军. 颗粒增强铝基复合材料力学性能影响因素及其性能预测模型发展趋势[J]. 材料导报, 2026, 40(17): 25060082-11.
ZHAO Dongcai, BAI Xuefeng, LENG Jinfeng, SUN Ji, ZHENG Jun. Influencing Factors on the Mechanical Properties of Particle-reinforced
Aluminum Matrix Composites and Development Trends of Their
Performance Prediction Models. Materials Reports, 2026, 40(17): 25060082-11.
链接本文:  
https://www.mater-rep.com/CN/10.11896/cldb.25060082  或          https://www.mater-rep.com/CN/Y2026/V40/I17/25060082
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