用机器学习算法预测轮胎加固钢丝中非金属夹杂物

M. Cuartas, E. Ruiz, D. Ferreño, J. Setién, V. Arroyo, F. Gutiérrez-Solana
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引用次数: 0

摘要

本研究旨在开发一种可靠的机器学习算法,根据实验确定的夹杂物的数量和性质对轮胎增强钢铸件进行分类。855件铸件可用于培训、验证和测试。在制造过程中监测了140个参数,这是分析的特点;输出是1或0,这取决于是否拒绝转换。采用了以下算法:逻辑回归、k近邻、支持向量分类器、随机森林、AdaBoost、梯度增强和人工神经网络。拒绝率的减小值意味着必须对不平衡数据集进行分类。对不平衡数据集(召回率、精度和AUC而不是准确性)使用了重采样方法和特定分数。随机森林是最成功的方法,在测试集中提供了0.85的曲线下面积。重新采样后没有发现明显的改善。实践证明,该工具可以选择不合格概率较高的样品,提高了质量控制的有效性。此外,优化后的随机森林能够识别最重要的特征,这些特征在冶金基础上得到了令人满意的解释。本研究旨在开发一种可靠的机器学习算法,根据实验确定的夹杂物的数量和性质对轮胎增强钢铸件进行分类。855件铸件可用于培训、验证和测试。在制造过程中监测了140个参数,这是分析的特点;输出是1或0,这取决于是否拒绝转换。采用了以下算法:逻辑回归、k近邻、支持向量分类器、随机森林、AdaBoost、梯度增强和人工神经网络。拒绝率的减小值意味着必须对不平衡数据集进行分类。对不平衡数据集(召回率、精度和AUC而不是准确性)使用了重采样方法和特定分数。随机森林是最成功的方法,在测试集中提供了0.85的曲线下面积。重新检测后未发现明显改善。
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Prediction of non-metallic inclusions in steel wires for tire reinforcement by means of machine learning algorithms
This study was aimed at developing a reliable Machine Learning algorithm to classify castings of steel for tire reinforcement depending on the number and properties of inclusions, experimentally determined. 855 castings were available for training, validation and testing. 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, 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 method providing an area under the curve in the test set of 0.85. No significant improvements were detected after resampling. It has been proved that this tool allows the samples with a higher probability of being rejected to be selected, 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.This study was aimed at developing a reliable Machine Learning algorithm to classify castings of steel for tire reinforcement depending on the number and properties of inclusions, experimentally determined. 855 castings were available for training, validation and testing. 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, 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 method providing an area under the curve in the test set of 0.85. No significant improvements were detected after resam...
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