From data to design: Random forest regression model for predicting mechanical properties of alloy steel
Samjukta Sinha, Prabhat Das
本研究研究了随机森林回归在预测合金钢-锯-Elongation,Tensile Strength和Yield Strength-from材料成分特征的力学性能中的应用,包括Iron(Fe),Chromium(Cr),Nickel(Ni),锰(Mn),硅(Si),Copper(Cu),Carbon(C)和冷轧过程中的变形百分比。 利用包含这些特征的数据集,我们训练并评估了随机森林模型,实现了R2分数和平均平方误差(MSE)的高预测性能。 结果表明该模型在提供准确预测方面的功效,该预测通过包括剩余图和学习曲线在内的各种性能指标进行验证。 研究结果强调了集成学习技术在增强材料属性预测方面的潜力,对材料科学中的工业应用产生了影响。
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 eviden...