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Recurrent NeuralNetwork (RNN)-BasedApproach to PredictMean Flow Stress inIndustrial Rolling Zinyagin Alexey G.Muntin Alexander V.Tynchenko Vadim S.2024 год

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

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База данных: Каталог библиотеки СФУ
Библиографическое описание: Recurrent Neural Network (RNN)-Based Approach to Predict Mean Flow Stress in Industrial Rolling : научное издание / Alexey G. Zinyagin [et al.]. - Текст : непосредственный // Metals. - 2024. - Т. 14, № 12. - P. 1329. - ISSN 2075-4701, DOI 10.3390/met14121329.
Аннотация: <jats:p>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.</jats:p>
Год издания: 2024
Авторы: Zinyagin Alexey G. , Muntin Alexander V. , Tynchenko Vadim S. , Zhikharev Pavel I. , Borisenko Nikita R. , Malashin Ivan
Издательство: MDPI AG
Источник: Metals
Выпуск: Т. 14, № 12
Номера страниц: 1329
Держатель оригинала документа: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Количество экземпляров:
Твёрдая копия издания отсутствует в фонде библиотеки

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