Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/14/12/1329 |
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| author | Alexey G. Zinyagin Alexander V. Muntin Vadim S. Tynchenko Pavel I. Zhikharev Nikita R. Borisenko Ivan Malashin |
| author_facet | Alexey G. Zinyagin Alexander V. Muntin Vadim S. Tynchenko Pavel I. Zhikharev Nikita R. Borisenko Ivan Malashin |
| author_sort | Alexey G. Zinyagin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cd3a3db4fdfb4e78aa8a779791a72f07 |
| institution | Kabale University |
| issn | 2075-4701 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj-art-cd3a3db4fdfb4e78aa8a779791a72f072024-12-27T14:39:45ZengMDPI AGMetals2075-47012024-11-011412132910.3390/met14121329Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial RollingAlexey G. Zinyagin0Alexander V. Muntin1Vadim S. Tynchenko2Pavel I. Zhikharev3Nikita R. Borisenko4Ivan Malashin5Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaThis 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.https://www.mdpi.com/2075-4701/14/12/1329flow stressmachine learningrolling millGRULSTMmaterial rheology |
| spellingShingle | Alexey G. Zinyagin Alexander V. Muntin Vadim S. Tynchenko Pavel I. Zhikharev Nikita R. Borisenko Ivan Malashin Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling Metals flow stress machine learning rolling mill GRU LSTM material rheology |
| title | Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling |
| title_full | Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling |
| title_fullStr | Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling |
| title_full_unstemmed | Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling |
| title_short | Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling |
| title_sort | recurrent neural network rnn based approach to predict mean flow stress in industrial rolling |
| topic | flow stress machine learning rolling mill GRU LSTM material rheology |
| url | https://www.mdpi.com/2075-4701/14/12/1329 |
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