Data from Prediction for Mechanical Properties of Lean Duplex Stainless Steel by Using Random Forest

Lean duplex stainless steel (LDSS), which is expected to apply to infrastructures, exhibits rounded shape of stress-strain curve. For this reason, a constitutive equation which is able to accurately express the curve is required for the ultimate strength analysis of LDSS structures. Authours have already proposed MRO curve as this kind of equation. However, not only 0.2% proof stress and tensile strength, which are specified in common material standard and a mill certificate, but also mechanical properties such as proportion limit etc are needed to describe the equation. In this study, we collected tension coupon test results of LDSS and created the simple estimated equation by means of linear regression analysis. Also, we predicted the me- chanical properties by using Random Forest (RF) which is one of machine learning method. According to comparison predicted results by RF with those by estimated equation, it was revealed that RF has same prediction accuracy of mechanical properties as estimation equation.

低碳双相不锈钢（LDSS）作为一种适用于基础设施的材料，呈现出应力-应变曲线的圆润形态。鉴于此，为了对LDSS结构进行极限强度分析，亟需一个能够精确表达该曲线的本构方程。研究者们已经提出了MRO曲线作为此类方程。然而，除了在常规材料标准和工厂证书中规定的0.2%抗拉强度和抗拉强度之外，还需要包括比例极限等机械性能来描述该方程。在本研究中，我们收集了LDSS的拉伸试件测试结果，并利用线性回归分析创建了简化的估算方程。此外，我们通过使用随机森林（RF）这一机器学习方法之一，预测了机械性能。通过将RF预测结果与估算方程的结果进行比较，发现RF在预测机械性能方面与估算方程具有相同的预测精度。