Abstract

This study explores the use of machine learning (ML) as a data-driven approach to estimate hot ductility of cast steel from literature data. Four ML algorithms were used to predict hot ductility by considering elemental composition and thermal conditions. Experimentally-measured reduction of area (RA) values were converted to a low-temperature limit, center-temperature, and high-temperature limit, which were represented as Gaussian curves. The prediction accuracy of the four ML models was evaluated using RMSE for these three output variables. In a case study of three steels that had different contents of alloying elements, only the Neural-net model predicted the RA trough more accurately in all cases. These results demonstrate the utility of ML models to predict hot ductility of cast steels.

Graphic Abstract

中文翻译：

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基于化学成分和热条件预测铸钢热延性的机器学习探索

摘要

这项研究探索了将机器学习（ML）作为一种数据驱动的方法来从文献数据中估算铸钢的热延展性的方法。通过考虑元素组成和热条件，使用了四种ML算法来预测热延展性。将实验测量的面积减小（RA）值转换为低温极限，中心温度和高温极限，以高斯曲线表示。使用RMSE对这三个输出变量评估了四个ML模型的预测准确性。在对三种合金元素含量不同的钢进行的案例研究中，只有神经网络模型可以在所有情况下更准确地预测RA谷。这些结果证明了ML模型可用于预测铸钢的热延展性。

图形摘要