Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures
This study is focused on the implementation of statistical learning methods for the prediction of the mechanical properties of steel products from the chemical profile of the raw material and the process parameters. The integration of this model into the production process allows a large-scale steel...
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MDPI AG
2025-02-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/2/75 |
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| author | Valerio Zippo Elisa Robotti Daniele Maestri Pietro Fossati David Valenza Stefano Maggi Gennaro Papallo Masho Hilawie Belay Simone Cerruti Giorgio Porcu Emilio Marengo |
| author_facet | Valerio Zippo Elisa Robotti Daniele Maestri Pietro Fossati David Valenza Stefano Maggi Gennaro Papallo Masho Hilawie Belay Simone Cerruti Giorgio Porcu Emilio Marengo |
| author_sort | Valerio Zippo |
| collection | DOAJ |
| description | This study is focused on the implementation of statistical learning methods for the prediction of the mechanical properties of steel products from the chemical profile of the raw material and the process parameters. The integration of this model into the production process allows a large-scale steel industry to predict steel properties with heightened accuracy, optimizing the manufacturing process for minimal waste and improved consistency. A workflow for process data analysis has been developed, based on the use of machine learning algorithms to build an interface for data treatment to be directly used online. The proposed approach has a comprehensive connotation, starting from data pre-treatment and cleaning, to model building and prediction. Different machine learning algorithms are compared (Polynomial Regression, LASSO, Random Forests and Gradient Boosting, ANN, SVM, and k-NN), to provide the best predictive ability, also exploiting human reinforcement. The results proved to be very promising for all the types of steel investigated, with very good RMSE and R<sup>2</sup> values both in fitting and in prediction. The application here presented is being integrated into Total Quality Tutor (TQT) software, developed in-house in C# language, for predicting the mechanical properties of steel. |
| format | Article |
| id | doaj-art-0c83166119e641bfb0e797fbfc0e8fef |
| institution | DOAJ |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-0c83166119e641bfb0e797fbfc0e8fef2025-08-20T02:45:31ZengMDPI AGTechnologies2227-70802025-02-011327510.3390/technologies13020075Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning ProceduresValerio Zippo0Elisa Robotti1Daniele Maestri2Pietro Fossati3David Valenza4Stefano Maggi5Gennaro Papallo6Masho Hilawie Belay7Simone Cerruti8Giorgio Porcu9Emilio Marengo10Acciaierie d’Italia S.p.A., Strada Boscomarengo 1, 15067 Novi Ligure, ItalyDepartment of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, ItalyAcciaierie d’Italia S.p.A., Strada Boscomarengo 1, 15067 Novi Ligure, ItalyAcciaierie d’Italia S.p.A., Strada Boscomarengo 1, 15067 Novi Ligure, ItalyAcciaierie d’Italia S.p.A., Strada Boscomarengo 1, 15067 Novi Ligure, ItalyAcciaierie d’Italia S.p.A., Strada Boscomarengo 1, 15067 Novi Ligure, ItalyAcciaierie d’Italia S.p.A., Strada Boscomarengo 1, 15067 Novi Ligure, ItalyDepartment of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, ItalyDepartment of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, ItalyAcciaierie d’Italia S.p.A., Strada Boscomarengo 1, 15067 Novi Ligure, ItalyDepartment of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, ItalyThis study is focused on the implementation of statistical learning methods for the prediction of the mechanical properties of steel products from the chemical profile of the raw material and the process parameters. The integration of this model into the production process allows a large-scale steel industry to predict steel properties with heightened accuracy, optimizing the manufacturing process for minimal waste and improved consistency. A workflow for process data analysis has been developed, based on the use of machine learning algorithms to build an interface for data treatment to be directly used online. The proposed approach has a comprehensive connotation, starting from data pre-treatment and cleaning, to model building and prediction. Different machine learning algorithms are compared (Polynomial Regression, LASSO, Random Forests and Gradient Boosting, ANN, SVM, and k-NN), to provide the best predictive ability, also exploiting human reinforcement. The results proved to be very promising for all the types of steel investigated, with very good RMSE and R<sup>2</sup> values both in fitting and in prediction. The application here presented is being integrated into Total Quality Tutor (TQT) software, developed in-house in C# language, for predicting the mechanical properties of steel.https://www.mdpi.com/2227-7080/13/2/75process optimizationsteelmechanical propertiesmachine learninggenetic algorithmartificial neural networks |
| spellingShingle | Valerio Zippo Elisa Robotti Daniele Maestri Pietro Fossati David Valenza Stefano Maggi Gennaro Papallo Masho Hilawie Belay Simone Cerruti Giorgio Porcu Emilio Marengo Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures Technologies process optimization steel mechanical properties machine learning genetic algorithm artificial neural networks |
| title | Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures |
| title_full | Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures |
| title_fullStr | Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures |
| title_full_unstemmed | Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures |
| title_short | Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures |
| title_sort | development of a self updating system for the prediction of steel mechanical properties in a steel company by machine learning procedures |
| topic | process optimization steel mechanical properties machine learning genetic algorithm artificial neural networks |
| url | https://www.mdpi.com/2227-7080/13/2/75 |
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