The selection of stamping process parameters has very important influence on the mechanical properties, production cost and production efficiency of forming parts.This paper is based on the Simufact Forming simulation platform, using the grey system theory, to design the orthogonal experimental scheme, with automotive aluminum alloy stamping parts, performed stamping forming experiments under different parameters, taking the forming part and equal effect force, equivalent plastic strain, flushing pressure and outer diameter deviation as evaluation indexes, by analyzing its integrated gray correlation degree, Multi-objective parameter optimization for aluminum alloy stamping parts, the optimal combination of process parameters and the influence order on the evaluation index are obtained, using the grey system theory to establish the GM (0, N) prediction model, realize the rapid prediction of the evaluation index of automobile aluminum alloy stamping forming parts.The results show that the influence order of process parameters on the evaluation index is: heat treatment temperature > friction coefficient > stamping speed; when the stamping speed is 5mm / s, friction coefficient is 0.1 and heat treatment temperature is 350℃, the mechanical properties of aluminum alloy stamping parts reach the optimal 50; GM (0, N) gray prediction model can predict the performance of alloy stamping parts, with simple modeling and small error.

Yu Dong. Optimization and Prediction of Aluminum Alloy Stamping Forming Parameters Based on Gray Correlation. International Journal of Frontiers in Engineering Technology (2023), Vol. 5, Issue 5: 68-75. https://doi.org/10.25236/IJFET.2023.050510.

[1] Jia Zhenyuan, Gu Feng, Wang Fuji, et al., the optimization of the processing process parameters of electric spark microholes based on the signal-to-noise ratio and gray correlation degree [J]. Journal of Mechanical Engineering, 2007,43 (7): 63-67.

[2] Xu Xianbo, Shao Hua. There are many turning parameters based on Tiankou algorithm and gray correlation theory Target optimization study [J]. Tools Technology, 2015,49 (8): 15-18.

[3] Li Haolin, Wang Jian. Optimization of plane grinding process parameters based on grey correlation analysis [J]. China Mechanical Engineering, 2011,22 (6): 631-635.

[4] Xin Min, Wang Xibin, Xie Lijing, et al. Optimization square of the milling parameters based on the grey theory Method study [J]. China Mechanical Engineering, 2009,20 (23): 2807-2810.

[5] Liu Jianpeng, Wang Zhenhu, Lin Qiquan, Li Luoxing, Direction Dong, Wang Yin. Optimization of stamping forming process parameters of aluminum substitute steel based on orthogonal test [J]. Journal of Plastic Engineering, 2018,25 (05): 110-116.

[6] Wang Huan. Research on the application of aluminum alloy plate in automobile production [J]. Time Motors, 2022, No.395 (23): 154-156.

[7] Xie Neming. Grey prediction: ideas, methods, and applications [J]. Journal of Nanjing University of Aeronautics and Astronautics (Social Science edition), 2022, 24(04): 11-18. DOI: 10.16297/j.nuaass. 202204002.