Machine Learning-Based Research on Tensile Strength of SiC-Reinforced Magnesium Matrix Composites via Stir Casting

Abstract

SiC is the most common reinforcement in magnesium matrix composites, and the tensile strength of SiC-reinforced magnesium 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.