Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network
In this work, the hot deformation behavior of 6A02 aluminum alloy was investigated by isothermal compression tests conducted in the temperature range of 683–783 K and strain-rate range of 0.001–1 s−1. According to the obtained true stress–true strain curves, the constitutive relationship of the allo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-03-01
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| Series: | Metals |
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| Online Access: | http://www.mdpi.com/2075-4701/7/4/114 |
| _version_ | 1818991654174982144 |
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| author | Ying Han Shun Yan Yu Sun Hua Chen |
| author_facet | Ying Han Shun Yan Yu Sun Hua Chen |
| author_sort | Ying Han |
| collection | DOAJ |
| description | In this work, the hot deformation behavior of 6A02 aluminum alloy was investigated by isothermal compression tests conducted in the temperature range of 683–783 K and strain-rate range of 0.001–1 s−1. According to the obtained true stress–true strain curves, the constitutive relationship of the alloy was revealed by establishing the Arrhenius-type constitutive model and back-propagation (BP) neural network model. It is found that the flow characteristic of 6A02 aluminum alloy is closely related to deformation temperature and strain rate, and the true stress decreases with increasing temperatures and decreasing strain rates. The hot deformation activation energy is calculated to be 168.916 kJ mol−1. The BP neural network model with one hidden layer and 20 neurons in the hidden layer is developed. The accuracy in prediction of the Arrhenius-type constitutive model and BP neural network model is eveluated by using statistics analysis method. It is demonstrated that the BP neural network model has better performance in predicting the flow stress. |
| first_indexed | 2024-12-20T20:13:42Z |
| format | Article |
| id | doaj.art-07220e28b9d44507b8015555bf541ca3 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-12-20T20:13:42Z |
| publishDate | 2017-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-07220e28b9d44507b8015555bf541ca32022-12-21T19:27:45ZengMDPI AGMetals2075-47012017-03-017411410.3390/met7040114met7040114Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural NetworkYing Han0Shun Yan1Yu Sun2Hua Chen3Key Laboratory of Advanced Structural Materials, Ministry of Education, Changchun University of Technology, Changchun 130012, ChinaKey Laboratory of Advanced Structural Materials, Ministry of Education, Changchun University of Technology, Changchun 130012, ChinaNational Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin 150001, ChinaKey Laboratory of Advanced Structural Materials, Ministry of Education, Changchun University of Technology, Changchun 130012, ChinaIn this work, the hot deformation behavior of 6A02 aluminum alloy was investigated by isothermal compression tests conducted in the temperature range of 683–783 K and strain-rate range of 0.001–1 s−1. According to the obtained true stress–true strain curves, the constitutive relationship of the alloy was revealed by establishing the Arrhenius-type constitutive model and back-propagation (BP) neural network model. It is found that the flow characteristic of 6A02 aluminum alloy is closely related to deformation temperature and strain rate, and the true stress decreases with increasing temperatures and decreasing strain rates. The hot deformation activation energy is calculated to be 168.916 kJ mol−1. The BP neural network model with one hidden layer and 20 neurons in the hidden layer is developed. The accuracy in prediction of the Arrhenius-type constitutive model and BP neural network model is eveluated by using statistics analysis method. It is demonstrated that the BP neural network model has better performance in predicting the flow stress.http://www.mdpi.com/2075-4701/7/4/114deformation behaviorconstitutive modelBP neural networkaluminum alloy |
| spellingShingle | Ying Han Shun Yan Yu Sun Hua Chen Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network Metals deformation behavior constitutive model BP neural network aluminum alloy |
| title | Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network |
| title_full | Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network |
| title_fullStr | Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network |
| title_full_unstemmed | Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network |
| title_short | Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network |
| title_sort | modeling the constitutive relationship of al 0 62mg 0 73si alloy based on artificial neural network |
| topic | deformation behavior constitutive model BP neural network aluminum alloy |
| url | http://www.mdpi.com/2075-4701/7/4/114 |
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