Optimization of the Fabrication of Cold Drawn Steel Wire Through Classification and Clustering Machine Learning Algorithms
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 im...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8846202/ |
| _version_ | 1818924065166983168 |
|---|---|
| author | Estela Ruiz Miguel Cuartas Diego Ferreno Laura Romero Valentin Arroyo Federico Gutierrez-Solana |
| author_facet | Estela Ruiz Miguel Cuartas Diego Ferreno Laura Romero Valentin Arroyo Federico Gutierrez-Solana |
| author_sort | Estela Ruiz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T02:19:24Z |
| format | Article |
| id | doaj.art-29729e1bd57b45a7ad235744f0b04fdb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T02:19:24Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-29729e1bd57b45a7ad235744f0b04fdb2022-12-21T19:56:51ZengIEEEIEEE Access2169-35362019-01-01714168914170010.1109/ACCESS.2019.29429578846202Optimization of the Fabrication of Cold Drawn Steel Wire Through Classification and Clustering Machine Learning AlgorithmsEstela Ruiz0Miguel Cuartas1Diego Ferreno2https://orcid.org/0000-0003-3533-1881Laura Romero3Valentin Arroyo4Federico Gutierrez-Solana5Global Steel Wire (GSW), Santander, SpainGroup of Information Technologies (GTI), University of Cantabria, Santander, SpainLaboratory of Science and Engineering of Materials Division (LADICIM), University of Cantabria, Santander, SpainLaboratory of Science and Engineering of Materials Division (LADICIM), University of Cantabria, Santander, SpainGroup of Information Technologies (GTI), University of Cantabria, Santander, SpainLaboratory of Science and Engineering of Materials Division (LADICIM), University of Cantabria, Santander, SpainThe 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.https://ieeexplore.ieee.org/document/8846202/Cold drawingsteel wiremachine learningclassificationclusteringimbalanced dataset |
| spellingShingle | Estela Ruiz Miguel Cuartas Diego Ferreno Laura Romero Valentin Arroyo Federico Gutierrez-Solana Optimization of the Fabrication of Cold Drawn Steel Wire Through Classification and Clustering Machine Learning Algorithms IEEE Access Cold drawing steel wire machine learning classification clustering imbalanced dataset |
| title | Optimization of the Fabrication of Cold Drawn Steel Wire Through Classification and Clustering Machine Learning Algorithms |
| title_full | Optimization of the Fabrication of Cold Drawn Steel Wire Through Classification and Clustering Machine Learning Algorithms |
| title_fullStr | Optimization of the Fabrication of Cold Drawn Steel Wire Through Classification and Clustering Machine Learning Algorithms |
| title_full_unstemmed | Optimization of the Fabrication of Cold Drawn Steel Wire Through Classification and Clustering Machine Learning Algorithms |
| title_short | Optimization of the Fabrication of Cold Drawn Steel Wire Through Classification and Clustering Machine Learning Algorithms |
| title_sort | optimization of the fabrication of cold drawn steel wire through classification and clustering machine learning algorithms |
| topic | Cold drawing steel wire machine learning classification clustering imbalanced dataset |
| url | https://ieeexplore.ieee.org/document/8846202/ |
| work_keys_str_mv | AT estelaruiz optimizationofthefabricationofcolddrawnsteelwirethroughclassificationandclusteringmachinelearningalgorithms AT miguelcuartas optimizationofthefabricationofcolddrawnsteelwirethroughclassificationandclusteringmachinelearningalgorithms AT diegoferreno optimizationofthefabricationofcolddrawnsteelwirethroughclassificationandclusteringmachinelearningalgorithms AT lauraromero optimizationofthefabricationofcolddrawnsteelwirethroughclassificationandclusteringmachinelearningalgorithms AT valentinarroyo optimizationofthefabricationofcolddrawnsteelwirethroughclassificationandclusteringmachinelearningalgorithms AT federicogutierrezsolana optimizationofthefabricationofcolddrawnsteelwirethroughclassificationandclusteringmachinelearningalgorithms |