A Comparative Study on Arrhenius Equations and BP Neural Network Models to Predict Hot Deformation Behaviors of a Hypereutectoid Steel
In order to predict the high-temperature deformation behavior of hypereutectoid steel, the hot compression tests were conducted in the strain rate range of (0.001~1) s<sup>-1</sup> and the deformation temperature range of (950~1100)°C. The experimental data were employed to de...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9058646/ |
| _version_ | 1818433212660056064 |
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| author | Ling Qiao Yong Deng Yuan Wang Jingchuan Zhu |
| author_facet | Ling Qiao Yong Deng Yuan Wang Jingchuan Zhu |
| author_sort | Ling Qiao |
| collection | DOAJ |
| description | In order to predict the high-temperature deformation behavior of hypereutectoid steel, the hot compression tests were conducted in the strain rate range of (0.001~1) s<sup>-1</sup> and the deformation temperature range of (950~1100)°C. The experimental data were employed to develop the Arrhenius constitutive model and BP neural network model, and their predictability for high temperature flow stress of hypereutectoid steel was further evaluated. Comparatively, a higher correlation coefficient (R) can be obtained for the BP neural network model compared with the Arrhenius constitutive equations. And the relative error within ±1% was more than 68.75% for the BP neural network model, while only 18.75% for the constitutive equations. The BP neural network model is considered more efficient and accurate to predict the hot deformation behavior than the Arrhenius constitutive equations. Moreover, the well-trained BP neural network model is employed to predict the flow stress varying with the deformation temperature and strain rate. The flow stress decreases with the increasing deformation temperature and decreasing strain rate, which is in accordance with the experimental evaluation. The results indicate that BP neural network model is an efficient tool for modelling and predicting the flow behavior of hypereutectoid steels in high temperature applications. |
| first_indexed | 2024-12-14T16:17:30Z |
| format | Article |
| id | doaj.art-52276080691f4d61bdf8e2bae9b7328d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T16:17:30Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-52276080691f4d61bdf8e2bae9b7328d2022-12-21T22:54:53ZengIEEEIEEE Access2169-35362020-01-018680836809010.1109/ACCESS.2020.29863899058646A Comparative Study on Arrhenius Equations and BP Neural Network Models to Predict Hot Deformation Behaviors of a Hypereutectoid SteelLing Qiao0https://orcid.org/0000-0002-5196-733XYong Deng1Yuan Wang2Jingchuan Zhu3School of Material Science and Engineering, Harbin Institute of Technology, Harbin, ChinaState Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaSchool of Material Science and Engineering, Harbin Institute of Technology, Harbin, ChinaIn order to predict the high-temperature deformation behavior of hypereutectoid steel, the hot compression tests were conducted in the strain rate range of (0.001~1) s<sup>-1</sup> and the deformation temperature range of (950~1100)°C. The experimental data were employed to develop the Arrhenius constitutive model and BP neural network model, and their predictability for high temperature flow stress of hypereutectoid steel was further evaluated. Comparatively, a higher correlation coefficient (R) can be obtained for the BP neural network model compared with the Arrhenius constitutive equations. And the relative error within ±1% was more than 68.75% for the BP neural network model, while only 18.75% for the constitutive equations. The BP neural network model is considered more efficient and accurate to predict the hot deformation behavior than the Arrhenius constitutive equations. Moreover, the well-trained BP neural network model is employed to predict the flow stress varying with the deformation temperature and strain rate. The flow stress decreases with the increasing deformation temperature and decreasing strain rate, which is in accordance with the experimental evaluation. The results indicate that BP neural network model is an efficient tool for modelling and predicting the flow behavior of hypereutectoid steels in high temperature applications.https://ieeexplore.ieee.org/document/9058646/Hypereutectoid steelhot deformation behaviorArrhenius equationsBP~neural network models |
| spellingShingle | Ling Qiao Yong Deng Yuan Wang Jingchuan Zhu A Comparative Study on Arrhenius Equations and BP Neural Network Models to Predict Hot Deformation Behaviors of a Hypereutectoid Steel IEEE Access Hypereutectoid steel hot deformation behavior Arrhenius equations BP~neural network models |
| title | A Comparative Study on Arrhenius Equations and BP Neural Network Models to Predict Hot Deformation Behaviors of a Hypereutectoid Steel |
| title_full | A Comparative Study on Arrhenius Equations and BP Neural Network Models to Predict Hot Deformation Behaviors of a Hypereutectoid Steel |
| title_fullStr | A Comparative Study on Arrhenius Equations and BP Neural Network Models to Predict Hot Deformation Behaviors of a Hypereutectoid Steel |
| title_full_unstemmed | A Comparative Study on Arrhenius Equations and BP Neural Network Models to Predict Hot Deformation Behaviors of a Hypereutectoid Steel |
| title_short | A Comparative Study on Arrhenius Equations and BP Neural Network Models to Predict Hot Deformation Behaviors of a Hypereutectoid Steel |
| title_sort | comparative study on arrhenius equations and bp neural network models to predict hot deformation behaviors of a hypereutectoid steel |
| topic | Hypereutectoid steel hot deformation behavior Arrhenius equations BP~neural network models |
| url | https://ieeexplore.ieee.org/document/9058646/ |
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