Prediction and Comparative Analysis of Peak Stress Using Optimized Constitutive and ATPSO-BP Models: A Case Study of 6016H18 Aluminum Alloy-Reference-Cited by-同舟云学术

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Prediction and Comparative Analysis of Peak Stress Using Optimized Constitutive and ATPSO-BP Models: A Case Study of 6016H18 Aluminum Alloy

Published:2024-02-20 Issue:4 Volume:53 Page:1933-1941
ISSN:0361-5235
Container-title:Journal of Electronic Materials
language:en
Short-container-title:J. Electron. Mater.

Author:

Zhang Jianping,Gu Jiahan,Li Jia,Fu Jian,Zhang Zhiwei

Funder

Foundation of Science and Technology Commission of Shanghai Municipality

Science and Technology Commission of Shanghai Municipality

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Shanghai Professional Technical Service Platform for Intelligent Operation and Maintenance of Renewable Energy

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11664-024-10945-w.pdf

Reference30 articles.

1. M. Iyota, T. Matsuda, T. Sano, M. Shigeta, T. Shobu, H. Yumoto, T. Koyama, H. Yamazaki, Y. Senba, H. Ohashi, S. Goto, I. Inoue, Y. Hayashi, K. Tamasaku, T. Osaka, J. Yamada, and M. Yabashi, A study on convection in molten zone of aluminum alloy during Fe/Al resistance spot welding. J. Manuf. Process. 94, 424 (2023).

2. Y.F. Qin, H.J. Jiang, Y.J. Cong, G.Y. Li, L. Qi, and J.J. Cui, Rivet die design and optimization for electromagnetic riveting of aluminium alloy joints. Eng. Optim. 53(5), 770 (2020).

3. Y.B. Zhang, Y.J. Cai, S. Liu, Z.J. Su, and T. Jiang, Life cycle assessment of aluminum-silicon alloy production from secondary aluminum in China. J. Clean. Prod. 392, 136214 (2023).

4. J.Y. Zhang and J. Peng, A review on aluminum alloy conductors influenced by alloying elements and thermomechanical treatments: Microstructure and properties. J. Mater. Res. 38(6), 1488 (2023).

5. E. Georgantzia, M. Gkantou, and G.S. Kamaris, Aluminium alloys as structural material: a review of research. Eng. Struct. 227, 111372 (2021).

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1. An improved extreme learning machine model for predicting the mechanical property of AZ80 magnesium alloy;Applied Physics A;2024-07-25

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