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.

SiC是镁基复合材料中最常见的增强体，SiC增强镁基复合材料的拉伸强度与SiC的分布密切相关。实现 SiC 的均匀分布需要对 SiC 的参数以及加工和制备过程进行精细控制。然而，由于可调参数众多，使用传统的实验方法需要进行大量的实验才能获得均匀分布的复合材料。因此，本研究采用机器学习方法来探讨SiC增强镁基复合材料在机械搅拌铸造过程中的拉伸强度。通过分析SiC参数和加工参数对复合材料性能的影响，我们建立了有效的预测模型。此外，还开发了六种不同的机器学习回归模型来预测 SiC 增强镁基复合材料的拉伸强度。通过验证和比较，我们的模型在预测复合材料的拉伸强度方面表现出良好的准确性和可靠性。研究结果表明，热挤压处理、SiC含量和搅拌时间对拉伸强度有显着影响。