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.
Highlights
Prestressed concrete is widely used for the construction of structural components such as bridges, buildings, water tanks or railways sleepers, among others [1]
In a prestressed concrete member, an initial compression is given to concrete by means of high strength steel wires that are subjected to tension before the element is placed in use; in this way, the areas of concrete which would normally go into tension from external loads lose some of their precompression
For the Random forest (RF) in the test set Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) = 0.871; this is equivalent to a probability of 87.1% that a randomly picked point of the positive class will have a higher score according to the classifier than a randomly picked point from the negative class [20]
Summary
Prestressed concrete is widely used for the construction of structural components such as bridges, buildings, water tanks or railways sleepers, among others [1]. Concrete is a material that displays high compressive strength but a negligible and highly scattered tensile strength. In a prestressed concrete member, an initial compression is given to concrete by means of high strength steel wires that are subjected to tension before the element is placed in use; in this way, the areas of concrete which would normally go into tension from external loads lose some of their precompression. Strength and a reasonable ductility is required in steel wires for prestressed concrete to guarantee structural integrity. This is achieved by means of a carefully selected chemical composition and a rigorous fabrication process [3]. The demanding plastic deformations the wire is subjected to during drawing result in the strengthening of the steel through a strain hardening mechanism [7]
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