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<p>Machine learning is employed to comprehensively analyze and predict the hardenability of 20CrMo steel. The hardenability dataset includes J9 and J15 hardenability values, chemical composition, and heat treatment parameters. Various machine learning models, including linear regression (LR), <i>k</i>-nearest neighbors (KNN), random forest (RF), and extreme Gradient Boosting (XGBoost), are employed to develop predictive&#xa0;models for the hardenability of 20CrMo steel. Among these models, the XGBoost model achieves the best performance, with coefficients of determination (<i>R</i><sup>2</sup>) of 0.941 and 0.946 for predicting J9 and J15 values, respectively. The predictions fall with <i>a</i> ± 2 HRC bandwidth for 98% of J9 cases and 99% of J15 cases. Additionally, SHapley Additive exPlanations (SHAP) analysis is used to identify the key elements that significantly influence the hardenability of the 20CrMo steel. The analysis revealed that alloying elements such as Si, Cr, C, N and Mo play significant roles in hardenability. The strengths and weaknesses of various machine learning models in predicting hardenability are also discussed.</p>

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Enhanced hardenability prediction in 20CrMo special steel via XGBoost model

  • 期刊论文

  • 发表日期：

    2025年3月6日

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  • De-xin Zhu,
  • Bin-bin Wang,
  • Hai-tao Zhao,
  • Sen Wu,
  • Fu-yong Li,
  • Sheng-yong Huang,
  • Hong-hui Wu,
  • Shui-ze Wang,
  • Chao-lei Zhang,
  • Jun-heng Gao,
  • Xin-ping Mao

摘要

Machine learning is employed to comprehensively analyze and predict the hardenability of 20CrMo steel. The hardenability dataset includes J9 and J15 hardenability values, chemical composition, and heat treatment parameters. Various machine learning models, including linear regression (LR), k-nearest neighbors (KNN), random forest (RF), and extreme Gradient Boosting (XGBoost), are employed to develop predictive models for the hardenability of 20CrMo steel. Among these models, the XGBoost model achieves the best performance, with coefficients of determination (R2) of 0.941 and 0.946 for predicting J9 and J15 values, respectively. The predictions fall with a ± 2 HRC bandwidth for 98% of J9 cases and 99% of J15 cases. Additionally, SHapley Additive exPlanations (SHAP) analysis is used to identify the key elements that significantly influence the hardenability of the 20CrMo steel. The analysis revealed that alloying elements such as Si, Cr, C, N and Mo play significant roles in hardenability. The strengths and weaknesses of various machine learning models in predicting hardenability are also discussed.