期刊
COMPUTATIONAL MATERIALS SCIENCE
卷 174, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.commatsci.2019.109472
关键词
Tensile strength; Plasticity; XGBoost; Particle swarm optimization
资金
- National Key R&D Program Joint Research on Advanced Technology and Application of Electric Vehicles based on China-US Cooperation [2016YFE0102200]
- National Natural Science Foundation of China [U1864207]
- National Youth Foundation of China [51605155]
Exploring the relationships between the properties of steels and their compositions and manufacturing parameters is extremely crucial and indispensable to understanding the science of materials, and subsequently developing new materials. Tensile strength and plasticity, as two important properties of steels, are key to the improvement and optimization of the mechanical properties of steels. In the present paper, we propose an optimization model combining XGBoost algorithm with improved PSO to address the continuous multivariable optimization problem. The main goal is to determine the mapping functions between the tensile strength and plasticity and their influencing factors, based on a diversity of machine learning models such as Linear Regression, SVM, XGBoost, etc. After evaluating the performance these models, we then select the XGBoost model with highest accuracy as the mapping function, which has not been done in previous studies. Moreover, the determined mapping function serves as the fitness value of particle swarm optimization, after which the tensile strength and plasticity optimization with many variables is realized. Finally, the experimental results are analyzed theoretically, and proven to be effective and reliable.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据