The Mechanical Performance Prediction of Steel Materials based on Random Forest

Shihao Wang, Xiangxiang Wu
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Abstract

The mechanical performance of steel materials is crucial for the design, selection, and application of materials. In order to better predict the mechanical performance through chemical composition and process parameters, this paper establishes a predictive model for the mechanical properties of steel materials based on the random forest algorithm. The model predicts yield strength, tensile strength, and elongation based on chemical composition and process parameters. The results indicate that the random forest algorithm model demonstrates excellent performance in predicting the mechanical properties of steel materials.
基于随机森林的钢铁材料力学性能预测
钢铁材料的机械性能对于材料的设计、选择和应用至关重要。为了更好地通过化学成分和工艺参数预测力学性能,本文基于随机森林算法建立了钢材料力学性能预测模型。该模型根据化学成分和工艺参数预测屈服强度、抗拉强度和伸长率。结果表明,随机森林算法模型在预测钢材料力学性能方面表现出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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