Skip to main content
Log in

Optimized design of tube hydroforming loading path using multi-objective differential evolution

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Loading path design is one of the main factors that influence the quality of tube hydroforming processes. The conflicts between different quality indicators can be solved using multi-objective optimization evolutionary algorithms. In this paper, we propose a multi-objective optimization using differential evolution to obtain the optimum cooperation between the internal pressure and end feeding process. In the optimization process, several finite element simulations are performed to set up a least-squares support vector machine-based response surface model, which significantly improves the accuracy. We used a pre-bent hydroforming case study to demonstrate that this method is effective, accurate, and reliable.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Imaninejad M, Subhash G, Loukus A (2005) Loading path optimization of tube hydroforming process. Int J Mach Tools Manuf 45(12):1504–1514

    Article  Google Scholar 

  2. Huang T, Song X, Liu M (2015) The optimization of the loading path for T-shape tube hydroforming using adaptive radial basis function. Int J Adv Manuf Technol 82(9–12):1843–1857

    Google Scholar 

  3. Yong Z, Chan LC, Chunguang W, Pei W (2009) Optimization for loading paths of tube hydroforming using a hybrid method. Mater Manuf Process 24(6):700–708

    Article  Google Scholar 

  4. Yang L, Tao Z, He Y (2014) Prediction of loading path for tube hydroforming with radial crushing by combining genetic algorithm and bisection method. Proc Inst Mech Eng B J Eng Manuf 229(1):110–121

    Article  Google Scholar 

  5. Aydemir A, de Vree JHP, Brekelmans WAM, Geers MGD, Sillekens WH, Werkhoven RJ (2005) An adaptive simulation approach designed for tube hydroforming processes. J Mater Process Technol 159(3):303–310

    Article  Google Scholar 

  6. Yang B, Zhang W, Lin Z (2006) A method to design the loading path for tube hydroforming process. J Shanghai Jiaotong Univ 40(6):0893

    Google Scholar 

  7. Kami A, Dariani BM (2011) Prediction of wrinkling in thin-walled tube push-bending process using artificial neural network and finite element method. Proc Inst Mech Eng Part B-J Eng Manuf 225(B10):1801–1812

    Article  Google Scholar 

  8. Wei L, Yuying Y (2008) Multi-objective optimization of sheet metal forming process using Pareto-based genetic algorithm. J Mater Process Technol 208(1):499–506

    Article  Google Scholar 

  9. An H, Green D, Johrendt J, Smith L (2013) Multi-objective optimization of loading path design in multi-stage tube forming using MOGA. Int J Mater Form 6(1):125–135

    Article  Google Scholar 

  10. Zhu F, Wang Z, Lv M (2015) Multi-objective optimization method of precision forging process parameters to control the forming quality. The International Journal of Advanced Manufacturing Technology 1–9

  11. Tonghai W, Sheng S, Dexiu M (1993) The research of tube bulging using polyurethane under compound external forces and its application. Adv Technol Plast 494–499

  12. Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Vol. 3. ICSI Berkeley

  13. Storn R (1996) On the usage of differential evolution for function optimization. in Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American. IEEE

  14. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  15. Yuan S, Liu G, Lang L (2003) Numerical simulation of wrinkling in hydroforming of aluminum alloy tubes. T Nonferr Metal Soc 13(S1):152–156

    Google Scholar 

  16. Havranek J (1977) The effect of mechanical properties of sheet steels on the wrinkling behaviour during deep drawing of conical shells. J Mech Work Technol 1(2):115–129

    Article  Google Scholar 

  17. Mezura-Montes E, Reyes-Sierra M, Coello CC (2008) Multi-objective optimization using differential evolution: a survey of the state-of-the-art, in advances in differential evolution, U. Chakraborty, Editor. Springer Berlin Heidelberg. p. 173–196

  18. Deb K (1999) Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol Comput 7(3):205–230

    Article  Google Scholar 

  19. Xue F, Sanderson AC, Graves RJ (2003) Pareto-based multi-objective differential evolution. in Evolutionary Computation, 2003. CEC'03. The 2003 Congress on. IEEE

  20. Chen X, Du W, Qian F (2014) Multi-objective differential evolution with ranking-based mutation operator and its application in chemical process optimization. Chemom Intell Lab Syst 136(1):85–96

    Article  Google Scholar 

  21. Suykens JA, Vandewalle J, De Moor B (2001) Optimal control by least squares support vector machines. Neural Netw 14(1):23–35

    Article  Google Scholar 

  22. Xuan L (2014) Study on bend-bulge forming technology of thin-walled elbow with small bending radius. Beihang Universtiy

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yu-long Ge.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ge, Yl., Li, Xx., Lang, Lh. et al. Optimized design of tube hydroforming loading path using multi-objective differential evolution. Int J Adv Manuf Technol 88, 837–846 (2017). https://doi.org/10.1007/s00170-016-8790-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-8790-2

Keywords

Navigation