Research on predicting the thermocompression deformation behavior of Mg–Li matrix composite using machine learning and traditional techniques

作者：Dandan Li, Xiaoyu Hou, Yangfan Liu, Linhao Gu, Jinhui Wang, Jiaxuan Ma, Xiaoqiang Li, Zhi Jia, Qichi Le, Dexue Liu, Xincheng Yin · 发表于：Journal of Materials Research and Technology · 年份：2024 · DOI：10.1016/j.jmrt.2024.11.090 · 被引用次数：5 · 研究领域：Aluminum Alloys Composites Properties、Aluminum Alloy Microstructure Properties、Metallurgy and Material Forming

Artificial intelligence and machine learning (ML) technologies have emerged as powerful tools for analyzing the thermal compression deformation behavior of metal matrix composites, offering significant potential to optimize their plastic deformation processing techniques. In this study, the Al 3 La/LAZ532 composite based on in-situ self-reaction technology was successfully prepared by adding La 2 O 3 particles. Then, the thermal compression flow behavior of the as-cast composite was comparatively researched using a traditional Arrhenius model and advanced machine learning methods (Linear Regression, AdaBoost, Random Forest, and XGBoost). The flow stresses were predicted under various thermal operating conditions, and the performance of all models was assessed using root mean square error (RMSE), coefficient of determination (R 2 ) and mean absolute error (MAE). Analysis shows that the Random Forest model outperforms traditional models and other ML methods in predictive accuracy, achieving an R 2 of 0.97, an MAE of 5.8, and an RMSE of 7.07. Additionally, the stable hot working zone for the composite at 300 °C and a strain rate of 0.001–0.01 s⁻ 1 can be identified by combining the microscopic structure with the macroscopic hot working map. This zone is characterized by a uniformly distributed, fine-grained structure with high levels of dynamic recrystallization (DRX). The texture with the <0001> axis of the grains aligned parallel to the compression direction (CD) is formed in ...