金属材料塑性本构模型建立研究进展
Research progress on establishing of plastic constitutive models for metal materials
夏天 1高志玉 2赵斐 1樊献金 1高思达3
作者信息
- 1. 辽宁工程技术大学 材料科学与工程学院,辽宁 阜新 123000
- 2. 沈阳理工大学 材料科学与工程学院,辽宁 沈阳 110159
- 3. 中汽研汽车检验中心(常州)有限公司,江苏 常州 213000
- 折叠
摘要
本构模型反映了流变应力与应变、变形温度和应变速率的对应关系,在设备选型、有限元仿真计算等方面具有重要应用.综述了几种主要的本构模型建立方法,即经验型本构模型、物理型本构模型以及近年来被广泛使用的机器学习型本构模型.经验型本构模型的建立过程简便,但其无法从微观层面解释材料变形的机理.相比之下,物理型本构模型的建立虽然复杂,但由于其包含了微观机制信息,预测精度优于经验型本构模型.机器学习型本构模型相较于前两种建模方法预测精度更高,建模方式更加简便.运用VOSviewer和CiteSpace文献计量软件对金属材料本构模型建模方法进行统计分析,得出相关领域国家、机构研究现况以及之间合作关系,分析表明中国对于金属材料本构模型领域研究最为活跃,采用机器学习进行本构建模的研究是近年来热点.并且随着人工智能技术的飞速发展,运用机器学习建立本构模型将是未来发展方向.
Abstract
The constitutive model reflects the corresponding relationship between flow stress and strain,deformation temperature and strain rate,and has important applications in equipment selection,finite element simulation calculation and other aspects.Several main methods for establishing constitutive models,namely empirical constitutive models,physical constitutive models,and machine learning constitutive models that have been widely used in recent years were reviewed.The establishing process of the empirical constitutive models is simple,but it is cannot explain the mechanism of material deformation at the micro level.In contrast,although the establishment of physical constitutive models is complex,its prediction accuracy is superior to empirical constitutive models due to its inclusion of micro-scopic mechanism information.Compared to the first two modeling methods,machine learning constitutive models have higher prediction accuracy and simpler modeling methods.Using VOSviewer and CiteSpace bibliometric software,the statistical analysis was conducted on the modeling methods of metal material constitutive models.The current research status and cooperation relationships among relevant countries and institutions in the field were obtained.The analysis shows that China is the most active in the research of metal material constitutive models,and the use of machine learning for constitutive modeling has been a hot topic in recent years.And with the rapid development of ar-tificial intelligence technology,using machine learning to establish constitutive models will be the future development direction.
关键词
金属材料/经验型本构模型/物理型本构模型/机器学习型本构模型/文献计量Key words
metal material/empirical constitutive model/physical constitutive model/machine learning constitutive model/bibliomet-rics引用本文复制引用
基金项目
辽宁省教育厅高等学校基本科研项目(LJKMZ20220593)
出版年
2024
NETL
NSTL
万方数据