Modeling of Flow Stress of As-Rolled 7075 Aluminum Alloy during Hot Deformation by Artificial Neural Network and Application
Abstract To analyze deformation behavior and construct a valid constitutive relationship using an ANN model. Hot compression studies on as-rolled 7075 aluminum alloy were carried out using a TA DIL805 D thermal simulator at a temperature ranging from 573 to 733 K and a strain rate ranging from 0.001...
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| Autor*in: |
Yang, Hongbin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© ASM International 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of materials engineering and performance - Springer US, 1992, 32(2022), 12 vom: 13. Okt., Seite 5666-5677 |
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| Übergeordnetes Werk: |
volume:32 ; year:2022 ; number:12 ; day:13 ; month:10 ; pages:5666-5677 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11665-022-07474-0 |
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| Katalog-ID: |
OLC2143959842 |
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| 520 | |a Abstract To analyze deformation behavior and construct a valid constitutive relationship using an ANN model. Hot compression studies on as-rolled 7075 aluminum alloy were carried out using a TA DIL805 D thermal simulator at a temperature ranging from 573 to 733 K and a strain rate ranging from 0.001 to 1.0 $ s^{−1} $. Subsequently, the predicted flow stress data were applied to perform finite element simulations of hot compression experiments. The correlation coefficient and error analysis findings reveal that the ANN model has a high prediction accuracy for flow stress. The effective strain distribution in the hot-compressed specimens is not uniform, and it gradually decreases from the center of the deformed specimen to the side and end faces. The uniformity of the effective strain distribution and the tendency of crack generation are influenced by deformation parameters. Within the experimental circumstances of this study, the optimal deformation parameters are obtained, which are the temperature above 653 K and the strain rate between 0.01 and 0.1 $ s^{−1} $. | ||
| 650 | 4 | |a 7075 aluminum alloy | |
| 650 | 4 | |a ANN model | |
| 650 | 4 | |a finite element simulation | |
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| 650 | 4 | |a hot deformation | |
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| 700 | 1 | |a Bu, Hengyong |4 aut | |
| 700 | 1 | |a Lu, Xin |4 aut | |
| 700 | 1 | |a Yang, Hongmei |4 aut | |
| 700 | 1 | |a Qian, Zhuo |4 aut | |
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10.1007/s11665-022-07474-0 doi (DE-627)OLC2143959842 (DE-He213)s11665-022-07474-0-p DE-627 ger DE-627 rakwb eng 620 660 670 VZ Yang, Hongbin verfasserin (orcid)0000-0003-2512-175X aut Modeling of Flow Stress of As-Rolled 7075 Aluminum Alloy during Hot Deformation by Artificial Neural Network and Application 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 2022 Abstract To analyze deformation behavior and construct a valid constitutive relationship using an ANN model. Hot compression studies on as-rolled 7075 aluminum alloy were carried out using a TA DIL805 D thermal simulator at a temperature ranging from 573 to 733 K and a strain rate ranging from 0.001 to 1.0 $ s^{−1} $. Subsequently, the predicted flow stress data were applied to perform finite element simulations of hot compression experiments. The correlation coefficient and error analysis findings reveal that the ANN model has a high prediction accuracy for flow stress. The effective strain distribution in the hot-compressed specimens is not uniform, and it gradually decreases from the center of the deformed specimen to the side and end faces. The uniformity of the effective strain distribution and the tendency of crack generation are influenced by deformation parameters. Within the experimental circumstances of this study, the optimal deformation parameters are obtained, which are the temperature above 653 K and the strain rate between 0.01 and 0.1 $ s^{−1} $. 7075 aluminum alloy ANN model finite element simulation flow stress hot deformation Li, Mengnie aut Bu, Hengyong aut Lu, Xin aut Yang, Hongmei aut Qian, Zhuo aut Enthalten in Journal of materials engineering and performance Springer US, 1992 32(2022), 12 vom: 13. Okt., Seite 5666-5677 (DE-627)131147366 (DE-600)1129075-4 (DE-576)033027250 1059-9495 nnns volume:32 year:2022 number:12 day:13 month:10 pages:5666-5677 https://doi.org/10.1007/s11665-022-07474-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 32 2022 12 13 10 5666-5677 |
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10.1007/s11665-022-07474-0 doi (DE-627)OLC2143959842 (DE-He213)s11665-022-07474-0-p DE-627 ger DE-627 rakwb eng 620 660 670 VZ Yang, Hongbin verfasserin (orcid)0000-0003-2512-175X aut Modeling of Flow Stress of As-Rolled 7075 Aluminum Alloy during Hot Deformation by Artificial Neural Network and Application 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 2022 Abstract To analyze deformation behavior and construct a valid constitutive relationship using an ANN model. Hot compression studies on as-rolled 7075 aluminum alloy were carried out using a TA DIL805 D thermal simulator at a temperature ranging from 573 to 733 K and a strain rate ranging from 0.001 to 1.0 $ s^{−1} $. Subsequently, the predicted flow stress data were applied to perform finite element simulations of hot compression experiments. The correlation coefficient and error analysis findings reveal that the ANN model has a high prediction accuracy for flow stress. The effective strain distribution in the hot-compressed specimens is not uniform, and it gradually decreases from the center of the deformed specimen to the side and end faces. The uniformity of the effective strain distribution and the tendency of crack generation are influenced by deformation parameters. Within the experimental circumstances of this study, the optimal deformation parameters are obtained, which are the temperature above 653 K and the strain rate between 0.01 and 0.1 $ s^{−1} $. 7075 aluminum alloy ANN model finite element simulation flow stress hot deformation Li, Mengnie aut Bu, Hengyong aut Lu, Xin aut Yang, Hongmei aut Qian, Zhuo aut Enthalten in Journal of materials engineering and performance Springer US, 1992 32(2022), 12 vom: 13. Okt., Seite 5666-5677 (DE-627)131147366 (DE-600)1129075-4 (DE-576)033027250 1059-9495 nnns volume:32 year:2022 number:12 day:13 month:10 pages:5666-5677 https://doi.org/10.1007/s11665-022-07474-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 32 2022 12 13 10 5666-5677 |
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10.1007/s11665-022-07474-0 doi (DE-627)OLC2143959842 (DE-He213)s11665-022-07474-0-p DE-627 ger DE-627 rakwb eng 620 660 670 VZ Yang, Hongbin verfasserin (orcid)0000-0003-2512-175X aut Modeling of Flow Stress of As-Rolled 7075 Aluminum Alloy during Hot Deformation by Artificial Neural Network and Application 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 2022 Abstract To analyze deformation behavior and construct a valid constitutive relationship using an ANN model. Hot compression studies on as-rolled 7075 aluminum alloy were carried out using a TA DIL805 D thermal simulator at a temperature ranging from 573 to 733 K and a strain rate ranging from 0.001 to 1.0 $ s^{−1} $. Subsequently, the predicted flow stress data were applied to perform finite element simulations of hot compression experiments. The correlation coefficient and error analysis findings reveal that the ANN model has a high prediction accuracy for flow stress. The effective strain distribution in the hot-compressed specimens is not uniform, and it gradually decreases from the center of the deformed specimen to the side and end faces. The uniformity of the effective strain distribution and the tendency of crack generation are influenced by deformation parameters. Within the experimental circumstances of this study, the optimal deformation parameters are obtained, which are the temperature above 653 K and the strain rate between 0.01 and 0.1 $ s^{−1} $. 7075 aluminum alloy ANN model finite element simulation flow stress hot deformation Li, Mengnie aut Bu, Hengyong aut Lu, Xin aut Yang, Hongmei aut Qian, Zhuo aut Enthalten in Journal of materials engineering and performance Springer US, 1992 32(2022), 12 vom: 13. Okt., Seite 5666-5677 (DE-627)131147366 (DE-600)1129075-4 (DE-576)033027250 1059-9495 nnns volume:32 year:2022 number:12 day:13 month:10 pages:5666-5677 https://doi.org/10.1007/s11665-022-07474-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 32 2022 12 13 10 5666-5677 |
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10.1007/s11665-022-07474-0 doi (DE-627)OLC2143959842 (DE-He213)s11665-022-07474-0-p DE-627 ger DE-627 rakwb eng 620 660 670 VZ Yang, Hongbin verfasserin (orcid)0000-0003-2512-175X aut Modeling of Flow Stress of As-Rolled 7075 Aluminum Alloy during Hot Deformation by Artificial Neural Network and Application 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 2022 Abstract To analyze deformation behavior and construct a valid constitutive relationship using an ANN model. Hot compression studies on as-rolled 7075 aluminum alloy were carried out using a TA DIL805 D thermal simulator at a temperature ranging from 573 to 733 K and a strain rate ranging from 0.001 to 1.0 $ s^{−1} $. Subsequently, the predicted flow stress data were applied to perform finite element simulations of hot compression experiments. The correlation coefficient and error analysis findings reveal that the ANN model has a high prediction accuracy for flow stress. The effective strain distribution in the hot-compressed specimens is not uniform, and it gradually decreases from the center of the deformed specimen to the side and end faces. The uniformity of the effective strain distribution and the tendency of crack generation are influenced by deformation parameters. Within the experimental circumstances of this study, the optimal deformation parameters are obtained, which are the temperature above 653 K and the strain rate between 0.01 and 0.1 $ s^{−1} $. 7075 aluminum alloy ANN model finite element simulation flow stress hot deformation Li, Mengnie aut Bu, Hengyong aut Lu, Xin aut Yang, Hongmei aut Qian, Zhuo aut Enthalten in Journal of materials engineering and performance Springer US, 1992 32(2022), 12 vom: 13. Okt., Seite 5666-5677 (DE-627)131147366 (DE-600)1129075-4 (DE-576)033027250 1059-9495 nnns volume:32 year:2022 number:12 day:13 month:10 pages:5666-5677 https://doi.org/10.1007/s11665-022-07474-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 32 2022 12 13 10 5666-5677 |
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10.1007/s11665-022-07474-0 doi (DE-627)OLC2143959842 (DE-He213)s11665-022-07474-0-p DE-627 ger DE-627 rakwb eng 620 660 670 VZ Yang, Hongbin verfasserin (orcid)0000-0003-2512-175X aut Modeling of Flow Stress of As-Rolled 7075 Aluminum Alloy during Hot Deformation by Artificial Neural Network and Application 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 2022 Abstract To analyze deformation behavior and construct a valid constitutive relationship using an ANN model. Hot compression studies on as-rolled 7075 aluminum alloy were carried out using a TA DIL805 D thermal simulator at a temperature ranging from 573 to 733 K and a strain rate ranging from 0.001 to 1.0 $ s^{−1} $. Subsequently, the predicted flow stress data were applied to perform finite element simulations of hot compression experiments. The correlation coefficient and error analysis findings reveal that the ANN model has a high prediction accuracy for flow stress. The effective strain distribution in the hot-compressed specimens is not uniform, and it gradually decreases from the center of the deformed specimen to the side and end faces. The uniformity of the effective strain distribution and the tendency of crack generation are influenced by deformation parameters. Within the experimental circumstances of this study, the optimal deformation parameters are obtained, which are the temperature above 653 K and the strain rate between 0.01 and 0.1 $ s^{−1} $. 7075 aluminum alloy ANN model finite element simulation flow stress hot deformation Li, Mengnie aut Bu, Hengyong aut Lu, Xin aut Yang, Hongmei aut Qian, Zhuo aut Enthalten in Journal of materials engineering and performance Springer US, 1992 32(2022), 12 vom: 13. Okt., Seite 5666-5677 (DE-627)131147366 (DE-600)1129075-4 (DE-576)033027250 1059-9495 nnns volume:32 year:2022 number:12 day:13 month:10 pages:5666-5677 https://doi.org/10.1007/s11665-022-07474-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 32 2022 12 13 10 5666-5677 |
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Modeling of Flow Stress of As-Rolled 7075 Aluminum Alloy during Hot Deformation by Artificial Neural Network and Application |
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Abstract To analyze deformation behavior and construct a valid constitutive relationship using an ANN model. Hot compression studies on as-rolled 7075 aluminum alloy were carried out using a TA DIL805 D thermal simulator at a temperature ranging from 573 to 733 K and a strain rate ranging from 0.001 to 1.0 $ s^{−1} $. Subsequently, the predicted flow stress data were applied to perform finite element simulations of hot compression experiments. The correlation coefficient and error analysis findings reveal that the ANN model has a high prediction accuracy for flow stress. The effective strain distribution in the hot-compressed specimens is not uniform, and it gradually decreases from the center of the deformed specimen to the side and end faces. The uniformity of the effective strain distribution and the tendency of crack generation are influenced by deformation parameters. Within the experimental circumstances of this study, the optimal deformation parameters are obtained, which are the temperature above 653 K and the strain rate between 0.01 and 0.1 $ s^{−1} $. © ASM International 2022 |
| abstractGer |
Abstract To analyze deformation behavior and construct a valid constitutive relationship using an ANN model. Hot compression studies on as-rolled 7075 aluminum alloy were carried out using a TA DIL805 D thermal simulator at a temperature ranging from 573 to 733 K and a strain rate ranging from 0.001 to 1.0 $ s^{−1} $. Subsequently, the predicted flow stress data were applied to perform finite element simulations of hot compression experiments. The correlation coefficient and error analysis findings reveal that the ANN model has a high prediction accuracy for flow stress. The effective strain distribution in the hot-compressed specimens is not uniform, and it gradually decreases from the center of the deformed specimen to the side and end faces. The uniformity of the effective strain distribution and the tendency of crack generation are influenced by deformation parameters. Within the experimental circumstances of this study, the optimal deformation parameters are obtained, which are the temperature above 653 K and the strain rate between 0.01 and 0.1 $ s^{−1} $. © ASM International 2022 |
| abstract_unstemmed |
Abstract To analyze deformation behavior and construct a valid constitutive relationship using an ANN model. Hot compression studies on as-rolled 7075 aluminum alloy were carried out using a TA DIL805 D thermal simulator at a temperature ranging from 573 to 733 K and a strain rate ranging from 0.001 to 1.0 $ s^{−1} $. Subsequently, the predicted flow stress data were applied to perform finite element simulations of hot compression experiments. The correlation coefficient and error analysis findings reveal that the ANN model has a high prediction accuracy for flow stress. The effective strain distribution in the hot-compressed specimens is not uniform, and it gradually decreases from the center of the deformed specimen to the side and end faces. The uniformity of the effective strain distribution and the tendency of crack generation are influenced by deformation parameters. Within the experimental circumstances of this study, the optimal deformation parameters are obtained, which are the temperature above 653 K and the strain rate between 0.01 and 0.1 $ s^{−1} $. © ASM International 2022 |
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| title_short |
Modeling of Flow Stress of As-Rolled 7075 Aluminum Alloy during Hot Deformation by Artificial Neural Network and Application |
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https://doi.org/10.1007/s11665-022-07474-0 |
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| author2 |
Li, Mengnie Bu, Hengyong Lu, Xin Yang, Hongmei Qian, Zhuo |
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Li, Mengnie Bu, Hengyong Lu, Xin Yang, Hongmei Qian, Zhuo |
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| doi_str |
10.1007/s11665-022-07474-0 |
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2024-07-03T19:11:46.128Z |
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