Application of Machine Learning Algorithms and SHAP for Prediction and Feature Analysis of Tempered Martensite Hardness in Low-Alloy Steels

Metals

Namhyuk Seo¹  Seok-Jae Lee¹  Seung Bae Son¹  Junhyub Jeon¹  Minsu Jung² 

[1] Division of Advanced Materials Engineering, Jeonbuk National University, Jeonju 54896, Korea;Intelligent Manufacturing R&D Department, Korea Institute of Industrial Technology, Siheung 15014, Korea;

关键词: low-alloy steels;    tempering;    tempered martensite hardness;    machine learning;    SHAP;   

DOI  :  10.3390/met11081159

来源: DOAJ

【 摘 要 】

The tempering of low-alloy steels is important for controlling the mechanical properties required for industrial fields. Several studies have investigated the relationships between the input and target values of materials using machine learning algorithms. The limitation of machine learning algorithms is that the mechanism of how the input values affect the output has yet to be confirmed despite numerous case studies. To address this issue, we trained four machine learning algorithms to control the hardness of low-alloy steels under various tempering conditions. The models were trained using the tempering temperature, holding time, and composition of the alloy as the inputs. The input data were drawn from a database of more than 1900 experimental datasets for low-alloy steels created from the relevant literature. We selected the random forest regression (RFR) model to analyze its mechanism and the importance of the input values using Shapley additive explanations (SHAP). The prediction accuracy of the RFR for the tempered martensite hardness was better than that of the empirical equation. The tempering temperature is the most important feature for controlling the hardness, followed by the C content, the holding time, and the Cr, Si, Mn, Mo, and Ni contents.

【 授权许可】

Unknown