Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling

Zinyagin, Alexey G.; Muntin, Alexander V.; Tynchenko, Vadim S.; Zhikharev, Pavel I.; Borisenko, Nikita R.; Malashin, Ivan

doi:10.3390/met14121329

This is an early access version, the complete PDF, HTML, and XML versions will be available soon.

Open AccessArticle

Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling

by

Alexey G. Zinyagin

,

Alexander V. Muntin

,

Vadim S. Tynchenko

^*

,

Pavel I. Zhikharev

,

Nikita R. Borisenko

and

Ivan Malashin

^*

Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia

^*

Authors to whom correspondence should be addressed.

Metals 2024, 14(12), 1329; https://doi.org/10.3390/met14121329

Submission received: 27 October 2024 / Revised: 21 November 2024 / Accepted: 22 November 2024 / Published: 24 November 2024

(This article belongs to the Special Issue Machine Learning Models in Metals)

Download Versions Notes

Abstract

This study addresses the usage of data from industrial plate mills to calculate the mean flow stress of different steel grades. Accurate flow stress values may optimize rolling technology, but the existing literature often provides coefficients like those in the Hensel–Spittel equation for a limited number of steel grades, whereas in modern production, the chemical composition may vary by thickness, customer requirements, and economic factors, making it necessary to conduct costly and labor-intensive laboratory studies. This research demonstrates that leveraging data from industrial rolling mills and employing machine learning (ML) methods can predict material rheological behavior without extensive laboratory research. Two modeling approaches are employed: Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures. The model comprising one GRU layer and two fully connected layers, each containing 32 neurons, yields the best performance, achieving a Root Mean Squared Error (RMSE) of 7.5 MPa for the predicted flow stress of three steel grades in the validation set.

Keywords: flow stress; machine learning; rolling mill; GRU; LSTM; material rheology

Share and Cite

MDPI and ACS Style

Zinyagin, A.G.; Muntin, A.V.; Tynchenko, V.S.; Zhikharev, P.I.; Borisenko, N.R.; Malashin, I. Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling. Metals 2024, 14, 1329. https://doi.org/10.3390/met14121329

AMA Style

Zinyagin AG, Muntin AV, Tynchenko VS, Zhikharev PI, Borisenko NR, Malashin I. Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling. Metals. 2024; 14(12):1329. https://doi.org/10.3390/met14121329

Chicago/Turabian Style

Zinyagin, Alexey G., Alexander V. Muntin, Vadim S. Tynchenko, Pavel I. Zhikharev, Nikita R. Borisenko, and Ivan Malashin. 2024. "Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling" Metals 14, no. 12: 1329. https://doi.org/10.3390/met14121329

APA Style

Zinyagin, A. G., Muntin, A. V., Tynchenko, V. S., Zhikharev, P. I., Borisenko, N. R., & Malashin, I. (2024). Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling. Metals, 14(12), 1329. https://doi.org/10.3390/met14121329

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI