ABSTRACT Analytical models based on physical metallurgy are of limited ability to predict the strength of steel due to the complexities of steelmaking. This paper presents the results obtained using Machine Learning procedures to predict the tensile strength of steel rods manufactured in an electric arc furnace. The available dataset includes 5540 observations (tensile tests) and 97 features (fabrication parameters) monitored during the different stages of the process (electric arc furnace, ladle furnace, continuous casting and hot rolling). The following regression algorithms have been implemented: Multiple Linear Regression, K-Nearest Neighbors, Classification and Regression Tree, three Ensemble Methods (Random Forest, Gradient Boosting and Adaboost) and Artificial Neural Networks. The fine-tuned Random Forest, provided an R2 of 0.775 and a mean absolute percentage error of 0.76% in the test dataset. After optimization, the Feature Importance and the Permutation Importance algorithms showed that chemical variables have the greater influence on the material strength. The quantitative influence of these variables was represented through Partial Dependence Plots. In short, this research has enabled validating a series of Machine Learning models that provide the necessary information for a correct decision-making to optimize the strength of the steel rods.

中文翻译：

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用于预测钢棒强度的机器学习算法：炼钢中数据驱动制造的示例

摘要 由于炼钢的复杂性，基于物理冶金学的分析模型预测钢的强度的能力有限。本文介绍了使用机器学习程序预测电弧炉中制造的钢棒的抗拉强度所获得的结果。可用的数据集包括在工艺的不同阶段（电弧炉、钢包炉、连铸和热轧）监测的 5540 个观察结果（拉伸试验）和 97 个特征（制造参数）。已实现以下回归算法：多元线性回归、K-最近邻、分类和回归树、三种集成方法（随机森林、梯度提升和 Adaboost）和人工神经网络。微调的随机森林提供了 0 的 R2。775 和测试数据集中 0.76% 的平均绝对百分比误差。优化后，特征重要性和排列重要性算法表明化学变量对材料强度的影响更大。这些变量的定量影响通过部分依赖图表示。简而言之，这项研究能够验证一系列机器学习模型，这些模型为正确决策以优化钢棒强度提供必要的信息。