首页|Machine Learning-Based Research on Tensile Strength of SiC-Reinforced Magnesium Matrix Composites via Stir Casting
Machine Learning-Based Research on Tensile Strength of SiC-Reinforced Magnesium Matrix Composites via Stir Casting
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
SiC is the most common reinforcement in magnesium matrix composites,and the tensile strength of SiC-reinforced mag-nesium matrix composites is closely related to the distribution of SiC.Achieving a uniform distribution of SiC requires fine control over the parameters of SiC and the processing and preparation process.However,due to the numerous adjustable parameters,using traditional experimental methods requires a considerable amount of experimentation to obtain a uniformly distributed composite material.Therefore,this study adopts a machine learning approach to explore the tensile strength of SiC-reinforced magnesium matrix composites in the mechanical stirring casting process.By analyzing the influence of SiC parameters and processing parameters on composite material performance,we have established an effective predictive model.Furthermore,six different machine learning regression models have been developed to predict the tensile strength of SiC-reinforced magnesium matrix composites.Through validation and comparison,our models demonstrate good accuracy and reliability in predicting the tensile strength of the composite material.The research findings indicate that hot extrusion treatment,SiC content,and stirring time have a significant impact on the tensile strength.
Beijing Advanced Innovation Center for Materials Genome Engineering,University of Science and Technology Beijing,Beijing 100083,China
Institute of Materials Intelligent Technology,Liaoning Academy of Materials,Shenyang 110004,China
School of Materials Science and Engineering,Collaborative Innovation Center of Ministry of Education and Shanxi Province for High-Performance Al/Mg Alloy Materials,North University of China,Taiyuan 030051,China
National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Defense Basic Scientific Research Program of ChinaKey Research and Development Program of Shanxi Province
万方数据
维普
