[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-128802-105":59,"doc-detail-128802-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","development-of-a-self-updating-system-for-the-prediction-of-steel-mechanical-properties-by-machine-learning-procedures","Development of a Self-Updating System for the Prediction of Steel Mechanical Properties - by Machine Learning Procedures","","This study implements statistical learning methods to predict the mechanical properties of steel products using the chemical profile of raw materials and process parameters. Embedding the model into the production environment enables large-scale, more accurate property prediction, improving manufacturing consistency while reducing waste. A complete online-ready workflow is presented, covering data pre-treatment and cleaning, model building, and prediction. Multiple machine learning methods are compared, yielding strong RMSE and R2 performance during both fitting and prediction. The approach is being integrated into the in-house Total Quality Tutor (TQT) software for steel property prediction.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/development-of-a-self-updating-system-for-the-prediction-of-steel-mechanical-properties-by-machine-learning-procedures/128802/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/development-of-a-self-updating-system-for-the-prediction-of-steel-mechanical-properties-by-machine-learning-procedures/128802.png","ImageObject",300,407,{"name":92,"@type":93},"Violet","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-19","2026-08-06",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What inputs are used to predict steel mechanical properties in this study?","Question",{"text":112,"@type":113},"Predictions use the chemical profile of the raw material and process parameters collected during production.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is the purpose of integrating the model into the steel production process?",{"text":117,"@type":113},"Integration supports large-scale prediction with higher accuracy, helping optimize manufacturing to minimize waste and improve consistency.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning algorithms are compared to find the best predictive ability?",{"text":121,"@type":113},"The study compares Polynomial Regression, LASSO, Random Forests, Gradient Boosting, ANN, SVM, and k-NN, and evaluates performance for multiple steel types.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},128802,1786003544,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},1099523885336,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Article  \nDevelopment of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures  \nValerio Zippo 1, Elisa Robotti 2, *, Daniele Maestri 1, Pietro Fossati 1, David Valenza 1, Stefano Maggi 1, Gennaro Papallo 1, Masho Hilawie Belay 2, Simone Cerruti 2, Giorgio Porcu 1,* and Emilio Marengo 2  \nAcademic Editor: George F. Fragulis  \nReceived: 24 December 2024  \nRevised: 4 February 2025  \nAccepted: 4 February 2025  \nPublished: 11 February 2025  \nCitation: Zippo, V.; Robotti, E.; Maestri, D.; Fossati, P.; Valenza, D.; Maggi, S.; Papallo, G.; Belay, M.H.; Cerruti, S.; Porcu, G.; et al. Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures. Technologies 2025, 13, 75 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)technologies13020075  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Acciaierie d’Italia S.p.A., Strada Boscomarengo 1, 15067 Novi Ligure, Italy; [valerio.zippo@adiinas.com](valerio.zippo@adiinas.com) (V.Z.); [daniele.maestri@adiinas.com](daniele.maestri@adiinas.com) (D.M.); [pietro.fossati@adiinas.com](pietro.fossati@adiinas.com) (P.F.); [davidvalenza@hotmail.it](davidvalenza@hotmail.it) (D.V.); [stefano_maggi@yahoo.it](stefano_maggi@yahoo.it) (S.M.); [gennaro.papallo@adiinas.com](gennaro.papallo@adiinas.com) (G.P.)  \n2 Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale Michel 11, 15121 Alessandria, Italy; [masho.belay@uniupo.it](masho.belay@uniupo.it) (M.H.B.); [simone.cerruti@uniupo.it](simone.cerruti@uniupo.it) (S.C.); [emilio.marengo@uniupo.it](emilio.marengo@uniupo.it) (E.M.)  \n* Correspondence: [elisa.robotti@uniupo.it](elisa.robotti@uniupo.it) (E.R.); [giorgio.porcu@adiinas.com](giorgio.porcu@adiinas.com) (G.P.); Tel.: +39-0131-360272 (E.R.);+39-3407-407982 (G.P.)  \nAbstract: 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 R2 values both in fitting and in prediction. The application herepresented is being integrated into Total Quality Tutor (TQT) software, developed in-house in C\\# language, for predicting the mechanical properties of steel.  \nKeywords: process optimization; steel; mechanical properties; machine learning; genetic algorithm; artificial neural networks  \n1. Introduction  \nIn the flat rolled steel industry, addressing the costs of scrapping non-compliant material and losing competitiveness due to quality or process issues is a key challenge. When a material does not meet required standards, the economic consequences can be substantial. Material rejection involves not only the direct","cbCaiihtvjVp5gZU","https://ap.wps.com/l/cbCaiihtvjVp5gZU","pdf",7795843,38,"English","# Introduction\n## Machine learning in steel production\n## Problem of scrap and quality non-compliance\n## Predictive analytics and optimization","[{\"question\":\"What inputs are used to predict steel mechanical properties in this study?\",\"answer\":\"Predictions use the chemical profile of the raw material and process parameters collected during production.\"},{\"question\":\"What is the purpose of integrating the model into the steel production process?\",\"answer\":\"Integration supports large-scale prediction with higher accuracy, helping optimize manufacturing to minimize waste and improve consistency.\"},{\"question\":\"Which machine learning algorithms are compared to find the best predictive ability?\",\"answer\":\"The study compares Polynomial Regression, LASSO, Random Forests, Gradient Boosting, ANN, SVM, and k-NN, and evaluates performance for multiple steel types.\"}]","Development of a Self-Updating System for the Prediction of Steel Mechanical Properties - by Machine Learning Procedures | PDF",96]