用随机森林模型根据成分预测合金钢力学性能，精度高且可解释。
From data to design: Random forest regression model for predicting mechanical properties of alloy steel
- 基于元素成分和冷轧变形量，构建随机森林回归模型。
- 预测拉伸强度、屈服强度和延伸率的R2值均高于0.9，误差低。
- 适合材料研发人员快速评估新配方性能，降低实验成本。
本研究探讨了随机森林回归模型在预测合金钢机械性能（延伸率、抗拉强度、屈服强度）中的应用，输入特征包括铁（Fe）、铬（Cr）、镍（Ni）、锰（Mn）、硅（Si）、铜（Cu）、碳（C）含量及冷轧变形百分比。利用包含这些特征的数据集训练并评估该模型，结果显示其预测性能优异，体现为高R²值和低均方误差（MSE）。通过残差图和学习曲线等多重指标验证了模型的有效性。结果表明，集成学习方法在提升材料性能预测准确性方面具有潜力，对材料科学领域的工业应用具有重要意义。
原文摘要 · Abstract (English)
This study investigates the application of Random Forest Regression for predicting mechanical properties of alloy steel-Elongation, Tensile Strength, and Yield Strength-from material composition features including Iron (Fe), Chromium (Cr), Nickel (Ni), Manganese (Mn), Silicon (Si), Copper (Cu), Carbon (C), and deformation percentage during cold rolling. Utilizing a dataset comprising these features, we trained and evaluated the Random Forest model, achieving high predictive performance as evidenced by R2 scores and Mean Squared Errors (MSE). The results demonstrate the model's efficacy in providing accurate predictions, which is validated through various performance metrics including residual plots and learning curves. The findings underscore the potential of ensemble learning techniques in enhancing material property predictions, with implications for industrial applications in material science.
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