Optimization of the Fabrication of Cold Drawn Steel Wire Through Classification and Clustering Machine Learning Algorithms

Abstract

The demanding deformations steel is subjected to during drawing may result in the breakage of the wire. The hypothesis of this research is that drawing failure is not a random event but can be predicted using a suitable approach. Machine Learning classification and clustering algorithms have been implemented to predict the probability of failure during drawing and to optimize the manufacturing conditions to reduce the failure rate. The following algorithms have been employed for classification: K-Nearest Neighbors, Random Forests and Artificial Neural Networks. The reduced value of the rejection rate implies that classification must be carried out on an imbalanced dataset. For this reason, resampling methods (undersampling, oversampling and SMOTE) and specific scores for imbalanced datasets were used. It was possible to obtain a qualified Random Forest classifier which provided satisfactory scores (ROC AUC of 0.824 and an average precision of 0.604 in the test dataset). This tool allows the heats with a higher probability of undergoing any breakage during drawing to be detected, thus improving the final quality of the product. K-means clustering (K = 4) has been successfully used in this study to identify those manufacturing conditions that minimize the number of breakages during drawing. The results of the clustering analysis show that the rate of heats undergoing failure may be reduced by a factor of 2.5.

Mechanical and tribo-metallurgical behavior of 17-4 precipitation hardening stainless steel affected by severe cold plastic deformation: a comprehensive review article

“…In terms of the simulation studies, there are numerous articles based on either commercial software such as Ansys, Abaqus, et cetera or in-house coding software such as Matlab, Arduino, Python et cetera that can be followed by the concept of machine learning and deep learning approaches to predict the possibility of any failure during or after cold working to optimize the manufacturing processes to decrease the failure rate. For instance, as for the implementation of classification and clustering algorithms, Ruiz et al [54] used the K-Nearest Neighbors (KNN), Random Forests, and Artificial Neural Networks algorithms for classification. The study showed that the heats, considering a greater possibility of undergoing any failure during wire drawing of steel, were detected and, consequently, caused to enhance the product final quality.…”

Machine Learning Methods for the Prediction of the Inclusion Content of Clean Steel Fabricated by Electric Arc Furnace and Rolling

“…The fabrication of cold forming steel comprises the following four major stages: electric arc furnace (EAF), ladle furnace (LF), continuous casting (CC) and hot rolling (HR). These are briefly described hereafter [23][24][25]:…”