A Review of Optimization Methods for Sheet Metal Forming Processes

Lingfeng Du; Pengguan Wang; Shiting Yang

doi:10.6919/ICJE.202606_12(6).0016

Authors

Lingfeng Du
Pengguan Wang
Shiting Yang

DOI:

https://doi.org/10.6919/ICJE.202606_12(6).0016

Keywords:

Sheet Metal Forming; Process Parameter Optimization; Numerical Simulation; Intelligent Algorithm; Multi-Objective Optimization.

Abstract

Sheet metal forming is a core manufacturing process in aerospace, automotive, and marine industries, where optimization of process parameters directly impacts product quality and production costs. This paper systematically reviews research progress in optimizing sheet metal forming process parameters, examining the current application status of traditional optimization methods, numerical simulation techniques, and intelligent optimization algorithms in this field. It focuses on exploring the principles and characteristics of typical optimization techniques such as finite element simulation, response surface methodology, genetic algorithms, neural networks, and multi-objective optimization. Furthermore, it provides an outlook on cutting-edge research directions like online monitoring and real-time control. Research indicates that intelligent optimization methods integrating digital twins, artificial intelligence, and big data will become a significant development trend in sheet metal forming process parameter optimization.

Downloads

Download data is not yet available.

References

[1] Bian, C. C. (2026). Research on forming processes of advanced high-strength steels for automotive applications. Auto Electric Parts, (5), 136–138.

[2] Xiao, X., Zhang, H. L., Zhang, S. J., et al. (2026). Forming mechanism and processes for aircraft skin components. China Metalforming Equipment & Manufacturing Technology, 61(2), 18–21.

[3] Zhang, C., Xu, Y., Shi, H. W., et al. (2026). Thinning law and process optimization of bending arch zones in hydroforming for Q235 steel automobile crossbeam. Forging & Stamping Technology, 51(4), 96–102+116.

[4] Che, L., Li, X. M., Wang, X. Y., et al. (2023). Deep drawing of 6061 aluminum alloy for aircraft oil spill. Journal of Netshape Forming Engineering, 15(3), 112–119.

[5] Li, Y., Zhao, B. L., & Li, G. J. (2023). Optimization of stamping process parameters of automobile muffler connecting flange. Internal Combustion Engine & Parts, (19), 33–35.

[6] Li, Z. X., Shu, X. D., Guo, D. L., et al. (2017). Analysis and optimization of process parameters in cold roll forming of C-channel steel. Journal of Harbin Engineering University, 38(3), 446–451.

[7] Zhang, Y. M., & Zhou, X. B. (2006). Parameter Optimization in Aircraft Skin Stretch Forming Process. Acta Aeronautica et Astronautica Sinica, (6), 1203–1208.

[8] Lang, L. H., Yang, X. Y., Sun, Z. Y., et al. (2015). Multi-objective optimization of hydroforming process parameters for automotive closure panels based on response surface method. Automotive Engineering, 37(4), 480–484.

[9] Zhang, H. W., Wang, Y. Z., & Wu, J. L. (2023). Multi-objective optimization of process parameters for tailor rolled blank box parts in hydro deep forming based on grey relation analysis. Journal of Netshape Forming Engineering, 15(2), 180–187.

[10] Li, Z. S., Chai, Y. D., Bai, Y., et al. (2026). Research on high-precision sheet metal forming process based on die optimization. Die & Mould Manufacture, 26(3), 1–4.

[11] Hu, P. D., & Guo, X. H. (2019). Optimization of processing parameters of ultra-precision turning of aluminum alloy by DOE. Machinery, 57(4), 100–103+109.

[12] Li, G. J., Yuan, S., Xu, X. D., et al. (2014). Customizing procedure of finite element analysis for sheet metal forming. Aeronautical Manufacturing Technology, (17), 100–103.

[13] Gong, C. Y., & Guo, N. X. (2025). Applications of surrogate models in multidisciplinary design optimization of aero engine. Aerospace Power, (2), 62–65.

[14] Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.

[15] Sun, Y. N., & Lin, W. B. (2018). Application of gradient descent method in machine learning. Journal of Suzhou University of Science and Technology (Natural Science Edition), 35(2), 26–31.

[16] Che, J. J., Li, M. Z., Sui, Z., et al. (2001). GA-based optimization of transition region for section forming. Journal of Jilin University (Engineering and Technology Edition), (2), 12–16. https://doi.org/10.13229/j.cnki.jdxbgxb2001.02.003

[17] Wang, C. C., & Zang, H. J. (2021). Application of particle swarm optimization algorithm in manufacturing scheduling system. Equipment Manufacturing Technology, (9), 21–24+30.

[18] Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

[19] Zitzler, E., Laumanns, M., & Thiele, L. (2002). SPEA2: Improving the strength pareto evolutionary algorithm. In K. Giannakoglou, D. T. Tsahalis, J. Periaux, et al. (Eds.), Proceedings of the Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems (pp. 95–100). Springer Verlag.

[20] Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731.

[21] Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279.

[22] Hu, Z. L., Wei, P. F., & Hu, L. (2026). Recent advances in high-quality and high-efficiency forming technologies for aluminum alloy components in transportation systems. Scientia Sinica (Technologica), 56(4), 825–861.

[23] Lv, L. Y., Lu, Y. J., Wang, S., et al. (2024). Survey and prospect of surrogate model technique and application. Journal of Mechanical Engineering, 60(3), 254–281.

[24] Zou, Z. P., Chen, Y. C., Yao, L. C., et al. (2025). A review of impact of multi-source uncertainties on aerodynamic and heat transfer performance of turbine. Journal of Propulsion Technology, 46(10), 6–49.

[25] Tang, J., Xia, H., Yu, W., et al. (2023). Research status and prospects of intelligent optimization control for municipal solid waste incineration process. Acta Automatica Sinica, 49(10), 2019–2059.

[26] Bi, C. B., Tang, Y. J., Luo, Y. H., et al. (2024). Review on critical problems in reinforcement learning methods applied in power system optimization and control scenarios. Proceedings of the CSEE, 44(1), 1–22.