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Research Article |

Development of Property Prediction Model for Hot Strip Mill Using Machine Learning Algorithms

Producing machine learning models to capture and comprehend the relationship between variables and mechanical properties of hot-rolled steels was the goal of this effort. Mechanical Property are impacted by a variety of variables throughout the process. The innovation that has been presented would significantly alter this process. Metrics of accuracy that are utilised in the process of evaluating the effectiveness of machine learning models. To deal with complexity and uncertainty, a number of machine learning models have been created for the ultimate tensile strength, yield strength, and elongation as functions of chemical elements and thermo-mechanical variables. Machine learning techniques such as multiple linear regression, random forest model, gradient boosting model and XGBoost are used to predict mechanical properties of hot rolled steel by specifying processing parameters such as chemical composition and various thermo mechanical variables. By changing one variable while holding the other variables constant, the models were utilised to interpret trends. Spearheaded a high-impact initiative at JSW Steel to create a cutting-edge property prediction model for their hot strip mill, enhancing operational efficiency and product quality.

Machine Learning Models, Hot Strip Mill, Chemical Composition, Rolling Processing Parameters, Mechanical Properties

APA Style

Muley, R., Priya, S. (2024). Development of Property Prediction Model for Hot Strip Mill Using Machine Learning Algorithms. Machine Learning Research, 9(1), 1-9.

ACS Style

Muley, R.; Priya, S. Development of Property Prediction Model for Hot Strip Mill Using Machine Learning Algorithms. Mach. Learn. Res. 2024, 9(1), 1-9. doi: 10.11648/j.mlr.20240901.11

AMA Style

Muley R, Priya S. Development of Property Prediction Model for Hot Strip Mill Using Machine Learning Algorithms. Mach Learn Res. 2024;9(1):1-9. doi: 10.11648/j.mlr.20240901.11

Copyright © 2024 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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