Boron steel is one of the most valuable lightweight steels for automobile due to its high strength after hot stamping and low cost. In order to ensure service performance of automobile parts, the steel is required to have good hardenability. A novel data-driven machine learning (ML) model has been established by using relevant material descriptors, including chemical composition and distance along the Jominy bar, to predict the hardenability curve of boron steel. By evaluating and comparing prediction results of different ML methods on training and test sets, random forest is found to be the optimal model with high correlation coefficient and low error. Moreover, the ML model performs better than JMatPro and empirical formula in terms of prediction accuracy and variation trend of hardenability curve. The optimal ML model combined with orthogonal design is employed to successfully design a press-hardening steel with good hardenability, i.e., 0.04% V-added boron steel. Therefore, this study demonstrates that ML can predict accurately and efficiently the hardenability curve of boron steel and guide the material design and heat treatment process of advanced boron steel.

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中文翻译：

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一种数据驱动的机器学习方法来预测硼钢的淬透性曲线并辅助合金设计

硼钢由于其热冲压后强度高、成本低，是最有价值的汽车轻质钢之一。为保证汽车零部件的使用性能，要求钢材具有良好的淬透性。通过使用相关材料描述符（包括化学成分和沿 Jominy 棒的距离）建立了一种新的数据驱动机器学习 (ML) 模型，以预测硼钢的淬透性曲线。通过评估和比较不同ML方法在训练集和测试集上的预测结果，发现随机森林是具有高相关系数和低误差的最优模型。此外，ML模型在预测精度和淬透性曲线变化趋势方面表现优于JMatPro和经验公式。采用最优ML模型结合正交设计，成功设计出一种淬透性好的冲压硬化钢，即0.04%V添加硼钢。因此，本研究表明，ML可以准确、高效地预测硼钢的淬透性曲线，指导先进硼钢的材料设计和热处理工艺。

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