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

Rushikesh Muley; Shanti Priya

doi:doi:10.11648/j.mlr.20240901.11

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

Rushikesh Muley^*, Shanti Priya

Published in Machine Learning Research (Volume 9, Issue 1)

Received: 18 October 2023 Accepted: 13 November 2023 Published: 11 January 2024

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Abstract

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.

Published in	Machine Learning Research (Volume 9, Issue 1)
DOI	10.11648/j.mlr.20240901.11
Page(s)	1-9
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

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

References

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[3]	Yongjun Lan, Dianzhong Li, Xiaochun Sha,, Yiyi Li, Prediction of Microstructure and Mechanical Properties of Hot Rolled Steel Strip, steel research int. 75 (2004) No. 7.
[4]	Qian Xie, Manu Suvarna, Jiali Li, Xinzhe Zhu, Jiajia Cai, Xiaonan Wang,―Online prediction of mechanical properties of hot rolled steel plate using Machine learning’, Materials and Design 197 (2021) 109201.
[5]	N. Sandhya, ValluripallySowmya, ChennakesavaRaoBandaru, G. Raghu Babu,―Prediction of Mechanical Properties of Steel using Data Science Techniques‖, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019.
[6]	N. S. Reddy, B. B. Panigrahi, J. Krishnaiah-Modeling Mechanical Properties of low carbon hot rolled steels- advances in intelligent system, Springer 2013.
[7]	Chaovarat Junpradub,-Mathematical Modeling to Predict the Mechanical Properties of Hot Rolled Steel Sheets, Proceedings of the International Conference on Industrial Engineering and Operations Management Bangkok, Thailand, March 5-7, 2019.
[8]	Noppon Jiratthanakul, Somrerk Chandra-ambhorn-Prediction of the Mechanical Properties of Hot-Rolled Low Carbon Steel Strips in Correlation to Chemical Compositions and Rolling Conditions -Key Engineering Materials Vols 462-463 (2011).
[9]	D. F. Sokolov, A. A. Ogoltcov, A. A. Vasilyev, N. G. Kolbasnikovand S. F. Sokolov- Modeling of Microstructure and Mechanical Propertiesof Hot Rolled Steels- Materials Science Forum Vol. 762.
[10]	Mehmet SiracOzerdemArtificial Neural Network approach to predict mechanical properties of hot rolled, nonresulfurized, AISI 10xx series carbon steel bars-journal of materials processing technology 199 (2008) 437–439.
[11]	ZHI-WEI XU 1, XIAO-MING LIU 2, AND KAI ZHANG2, ―Mechanical Properties Prediction for Hot Rolled Alloy Steel Using Convolutional Neural Network‖ Digital Object Identifier 10.1109/ACCESS.2019.2909586.
[12]	Heung Nam Han, Jae Kon Lee, Hong Joon Kim, Young-Sool Jin-A model for deformation, temperature and phase transformation behavior of steels on run-out table in hot strip mill-Journal of Materials Processing Technology 128 (2002) 216–225.
[13]	Weigang Li Lu Xie -Prediction model for mechanical properties of hot rolled strips by deep learning-J. Iron Steel Res. Int, springer, 2019.
[14]	Bleck, W., Meyer, L. and Kasper, R., Stahl u. Eisen, 110: 26, 1990.
[15]	DeAdro, A. J., Conference high strength low alloy steels, ed. Dunne, D. P. and Chandra, T., Wollongong: University of Wollongong, 70, 1984.
[16]	Zrnik, J., Kvackaj, T., Sripinproach, D. and Sricharoenchai, P., Influence of plastic deformation condition on structure evolution in Nb-Ti microalloyedSteel, Journal of Materials Processing Technology, 133: 236–242, 2003.

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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. https://doi.org/10.11648/j.mlr.20240901.11

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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

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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

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@article{10.11648/j.mlr.20240901.11,
  author = {Rushikesh Muley and Shanti Priya},
  title = {Development of Property Prediction Model for Hot Strip Mill Using Machine Learning Algorithms},
  journal = {Machine Learning Research},
  volume = {9},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.mlr.20240901.11},
  url = {https://doi.org/10.11648/j.mlr.20240901.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20240901.11},
  abstract = {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.
},
 year = {2024}
}

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TY  - JOUR
T1  - Development of Property Prediction Model for Hot Strip Mill Using Machine Learning Algorithms
AU  - Rushikesh Muley
AU  - Shanti Priya
Y1  - 2024/01/11
PY  - 2024
N1  - https://doi.org/10.11648/j.mlr.20240901.11
DO  - 10.11648/j.mlr.20240901.11
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20240901.11
AB  - 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.

VL  - 9
IS  - 1
ER  -

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Author Information

Rushikesh Muley

Machine Learning, College of Engineering, Pune, India

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Shanti Priya

Machine Learning, Andhra University, Visakhapatnam, India

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Plain Text BibTeX RIS

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. https://doi.org/10.11648/j.mlr.20240901.11

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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

Copy | Download

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

Copy | Download

@article{10.11648/j.mlr.20240901.11,
  author = {Rushikesh Muley and Shanti Priya},
  title = {Development of Property Prediction Model for Hot Strip Mill Using Machine Learning Algorithms},
  journal = {Machine Learning Research},
  volume = {9},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.mlr.20240901.11},
  url = {https://doi.org/10.11648/j.mlr.20240901.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20240901.11},
  abstract = {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.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Development of Property Prediction Model for Hot Strip Mill Using Machine Learning Algorithms
AU  - Rushikesh Muley
AU  - Shanti Priya
Y1  - 2024/01/11
PY  - 2024
N1  - https://doi.org/10.11648/j.mlr.20240901.11
DO  - 10.11648/j.mlr.20240901.11
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20240901.11
AB  - 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.

VL  - 9
IS  - 1
ER  -

Copy | Download