Azala

Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures

Gorde:
Argitaratua izan da:Technologies vol. 13, no. 2 (2025), p. 75
Egile nagusia: Zippo, Valerio
Beste egile batzuk: Robotti, Elisa, Maestri, Daniele, Fossati, Pietro, Valenza, David, Maggi, Stefano, Papallo, Gennaro, Masho Hilawie Belay, Cerruti, Simone, Porcu, Giorgio, Marengo, Emilio
Argitaratua:
MDPI AG
Gaiak:
Italy
Mechanical properties
Cold
Accuracy
Datasets
Optimization techniques
Iron compounds
Workflow
Polynomials
Steel production
Steel industry
Raw materials
Manufacturing
Machine learning
Statistical analysis
Hot rolling
Energy consumption
Efficiency
Data analysis
Tensile strength
Product quality
Sustainable development
Corporate learning
Carbon
Temperature
Iron and steel industry
Process controls
Ductility
Statistical methods
Algorithms
Cost control
Process parameters
Sarrera elektronikoa:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiketak: Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
Laburpena: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 here presented is being integrated into Total Quality Tutor (TQT) software, developed in-house in C# language, for predicting the mechanical properties of steel.
ISSN:2227-7080
DOI:10.3390/technologies13020075
Baliabidea:Materials Science Database