A Study on Prediction of Process Parameters of Shot Peen Forming Using Artificial Neural Network Optimized by Genetic Algorithm

Author:

Wang T.,Wang J. B.,Zhang X. J.,Liu C.

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference36 articles.

1. Kulkarni, K.M.; Schey, J.A.; Badger, D.V.: Investigation of shot peening as a forming process for aircraft wing skins. J. Appl. Metalwork. 1(4), 34–44 (1981). https://doi.org/10.1007/BF02834344

2. VanLuchene, R.D.; Johnson, J.; Carpenter, R.G.: Induced stress relationships for wing skin forming by shot peening. J. Mater. Eng. Perform. 4(3), 283–290 (1995). https://doi.org/10.1007/BF02649065

3. Delijaicov, S.; Fleury, A.; Martins, F.: Application of multiple regression and neural networks to synthesize a model for peen forming process planning. J. Achiev. Mater. Manuf. Eng. 43(2), 651–656 (2010)

4. Xuepiao, B.; Yuansong, Z.; Qiang, M.; Enguang, H.: Experimental investigation on shot peen forming of 2198 Al-Li alloy plate. In: Proceedings of ICSP12 (2014)

5. Huang, X.; Zeng, Y.: Investigation on pre-stress shot peen forming process of aluminum alloy 2024-T351. In: Proceedings of ICSP12 (2014)

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