Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model
To realize the purpose of energy saving, materials with high weight are replaced by low-weight materials with eligible mechanical properties in all kinds of fields. Therefore, conducting research works on lightweight materials under specified work conditions is extremely important and profound. To u...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/11/3788 |
| _version_ | 1811153738024353792 |
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| author | Sijia Li Wenning Chen Krishna Singh Bhandari Dong Won Jung Xuewen Chen |
| author_facet | Sijia Li Wenning Chen Krishna Singh Bhandari Dong Won Jung Xuewen Chen |
| author_sort | Sijia Li |
| collection | DOAJ |
| description | To realize the purpose of energy saving, materials with high weight are replaced by low-weight materials with eligible mechanical properties in all kinds of fields. Therefore, conducting research works on lightweight materials under specified work conditions is extremely important and profound. To understand the relationship of aluminum alloy AA5005 among flow stress, true strain, strain rate, and deformation temperature, hot isothermal tensile tests were conducted within the strain rate range 0.0003–0.03 s<sup>−1</sup> and temperature range 633–773 K. Based on the true stress-true strain curves obtained from the experiment, a traditional constitutive regression Arrhenius-type equation was utilized to regress flow behaviors. Meanwhile, the Arrhenius-type equation was optimized by a sixth-order polynomial function for compensating strain. Thereafter, a back propagation artificial neural network (BP-ANN) model based on supervised machine learning was also employed to regress and predict flow stress in diverse deform conditions. Ultimately, by introducing statistical analyses correlation coefficient (R<sup>2</sup>), average absolute relative error (AARE), and relative error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>) to the comparative study, it was found that the Arrhenius-type equation will lose accuracy in cases of high stress. Additionally, owning higher R<sup>2</sup>, lower AARE, and more concentrative <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula> value distribution, the BP-ANN model is superior in regressing and predicting than the Arrhenius-type constitutive equation. |
| first_indexed | 2024-03-10T01:09:20Z |
| format | Article |
| id | doaj.art-720dabcd92f6485aab0cd839860c3630 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T01:09:20Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-720dabcd92f6485aab0cd839860c36302023-11-23T14:20:15ZengMDPI AGMaterials1996-19442022-05-011511378810.3390/ma15113788Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) ModelSijia Li0Wenning Chen1Krishna Singh Bhandari2Dong Won Jung3Xuewen Chen4Department of Mechanical Engineering, Jeju National University, Jeju-si 63243, KoreaDepartment of Mechanical Engineering, Jeju National University, Jeju-si 63243, KoreaDepartment of Mechanical Engineering, Jeju National University, Jeju-si 63243, KoreaDepartment of Mechanical Engineering, Jeju National University, Jeju-si 63243, KoreaSchool of Materials Science and Engineering, Henan University of Science and Technology, 263 Kaiyuan Avenue, Luoyang 471023, ChinaTo realize the purpose of energy saving, materials with high weight are replaced by low-weight materials with eligible mechanical properties in all kinds of fields. Therefore, conducting research works on lightweight materials under specified work conditions is extremely important and profound. To understand the relationship of aluminum alloy AA5005 among flow stress, true strain, strain rate, and deformation temperature, hot isothermal tensile tests were conducted within the strain rate range 0.0003–0.03 s<sup>−1</sup> and temperature range 633–773 K. Based on the true stress-true strain curves obtained from the experiment, a traditional constitutive regression Arrhenius-type equation was utilized to regress flow behaviors. Meanwhile, the Arrhenius-type equation was optimized by a sixth-order polynomial function for compensating strain. Thereafter, a back propagation artificial neural network (BP-ANN) model based on supervised machine learning was also employed to regress and predict flow stress in diverse deform conditions. Ultimately, by introducing statistical analyses correlation coefficient (R<sup>2</sup>), average absolute relative error (AARE), and relative error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>) to the comparative study, it was found that the Arrhenius-type equation will lose accuracy in cases of high stress. Additionally, owning higher R<sup>2</sup>, lower AARE, and more concentrative <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula> value distribution, the BP-ANN model is superior in regressing and predicting than the Arrhenius-type constitutive equation.https://www.mdpi.com/1996-1944/15/11/3788AA5005 alloyhigh temperatureArrhenius-typeflow stresslow strain rateBP-ANN |
| spellingShingle | Sijia Li Wenning Chen Krishna Singh Bhandari Dong Won Jung Xuewen Chen Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model Materials AA5005 alloy high temperature Arrhenius-type flow stress low strain rate BP-ANN |
| title | Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model |
| title_full | Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model |
| title_fullStr | Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model |
| title_full_unstemmed | Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model |
| title_short | Flow Behavior of AA5005 Alloy at High Temperature and Low Strain Rate Based on Arrhenius-Type Equation and Back Propagation Artificial Neural Network (BP-ANN) Model |
| title_sort | flow behavior of aa5005 alloy at high temperature and low strain rate based on arrhenius type equation and back propagation artificial neural network bp ann model |
| topic | AA5005 alloy high temperature Arrhenius-type flow stress low strain rate BP-ANN |
| url | https://www.mdpi.com/1996-1944/15/11/3788 |
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