Non-metallic inclusions are unavoidably produced during steel casting resulting in lower mechanical strength and other detrimental effects. This study was aimed at developing a machine learning algorithm to classify castings of steel for tire reinforcement depending on the number and properties of inclusions, experimentally determined. 855 observations were available for training, validation and testing the algorithms, obtained from the quality control of the steel. 140 parameters are monitored during fabrication, which are the features of the analysis; the output is 1 or 0 depending on whether the casting is rejected or not. The following algorithms have been employed: Logistic Regression, K-Nearest Neighbors, Support Vector Classifier (linear and RBF kernels), Random Forests, AdaBoost, Gradient Boosting and Artificial Neural Networks. The reduced value of the rejection rate implies that classification must be carried out on an imbalanced dataset. Resampling methods and specific scores for imbalanced datasets (recall, precision and AUC rather than accuracy) were used. Random Forest was the most successful algorithm providing an AUC in the test set of 0.85. No significant improvements were detected after resampling. The optimized Random Forest allows the samples with a higher probability of being rejected to be selected, thus improving the effectiveness of the quality control. In addition, the optimized Random Forest has enabled to identify the most important features, which have been satisfactorily interpreted on a metallurgical basis.

在铸钢过程中不可避免地会产生非金属夹杂物，从而导致较低的机械强度和其他不利影响。这项研究旨在开发一种机器学习算法，以根据实验确定的夹杂物的数量和性质对用于轮胎加固的铸钢件进行分类。可从钢的质量控制中获得855个观察值用于训练，验证和测试算法。在制造过程中监视140个参数，这是分析的特征；根据铸造是否被拒绝，输出为1或0。已采用以下算法：逻辑回归，K最近邻，支持向量分类器（线性和RBF核），随机森林，AdaBoost，梯度提升和人工神经网络。拒绝率的降低值意味着必须在不平衡的数据集上进行分类。使用了不平衡数据集的重采样方法和特定分数（召回率，精度和AUC而非准确性）。随机森林是在0.85的测试集中提供AUC的最成功算法。重采样后未检测到显着改善。优化的随机森林允许选择具有较高拒绝可能性的样本，从而提高质量控制的有效性。此外，经过优化的随机森林能够识别最重要的特征，这些特征已在冶金学上得到了令人满意的解释。使用了不平衡数据集的重采样方法和特定分数（查全率，精度和AUC而非准确性）。随机森林是在0.85的测试集中提供AUC的最成功算法。重采样后未检测到显着改善。经过优化的随机森林可以选择拒绝可能性更高的样本，从而提高质量控制的效率。此外，经过优化的随机森林能够识别最重要的特征，这些特征已在冶金学上得到了令人满意的解释。使用了不平衡数据集的重采样方法和特定分数（查全率，精度和AUC而非准确性）。随机森林是在0.85的测试集中提供AUC的最成功算法。重采样后未检测到显着改善。优化的随机森林允许选择具有较高拒绝可能性的样本，从而提高质量控制的有效性。此外，经过优化的随机森林能够识别最重要的特征，这些特征在冶金学上已得到令人满意的解释。优化的随机森林允许选择具有较高拒绝可能性的样本，从而提高质量控制的有效性。此外，优化的随机森林使您能够识别最重要的特征，这些特征已在冶金学上得到了令人满意的解释。优化的随机森林允许选择具有较高拒绝可能性的样本，从而提高质量控制的有效性。此外，经过优化的随机森林能够识别最重要的特征，这些特征在冶金学上已得到令人满意的解释。