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The optimization of the loading path for T-shape tube hydroforming using adaptive radial basis function

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Abstract

The success of the T-shape tube hydroforming process requires a combination of internal pressure and axial and counter punch actions. The objective of this study is to introduce the adaptive radial basis function and demonstrate its accuracy and efficiency through a numerical example, to determine the optimal loading parameters in T-shape tube hydroforming process. The finite element model is developed with the explicit dynamic finite element code LS-DYNA and validated against the experimental work. The Taguchi method based on variance of analysis technique is used to screen the important loading parameters, which have a significant effect on the forming quality, such as the maximum thinning ratio, protrusion height and contact area, etc, from a number of potential loading parameters. The contact area is considered as the objective while the maximum thinning ratio and protrusion height are regarded as the constraints, and then the optimal loading parameters are obtained by adaptive radial basis function after several iterations. The results show that a significant improvement in the contact area is achieved while the results of the minimum thickness and protrusion height do not become worse.

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References

  1. Ahmetoglu M, Altan T (2000) Tube hydroforming: state-of-the-art and future trends. J Mater Process Technol 98:25–33

    Article  Google Scholar 

  2. Dohmann F, Hartl CH (1996) Hydroforming—a method to manufacture lightweight parts. J Mater Process Technol 60:669–676

    Article  Google Scholar 

  3. Asnafi N (1999) Analytical modelling of tube hydroforming. Thin-Walled Struct 34:295–330

    Article  Google Scholar 

  4. Asnafi N, Skogsgardh A (2000) Theoretical and experimental analysis of stroke-controlled tube hydroforming. J Mater Sci A279:95–110

    Article  Google Scholar 

  5. Yang C, Ngaile G (2008) Analytical model for planar tube hydroforming: prediction of formed shape, corner fill, wall thinning, and forming pressure. Int J Mech Sci 50:1263–1279

    Article  MATH  Google Scholar 

  6. Alzahrani B, Ngaile G, Yang C (2013) Part 1: analytical modeling of symmetric multi-nose tube hydroforming. J Manuf Process 15:273–286

    Article  Google Scholar 

  7. Alzahrani B, Ngaile G (2013) Part 2: analytical modeling of regular planar polygon tube hydroforming as a special case of symmetric multi-nose tube hydroforming. J Manuf Process 15:287–297

    Article  Google Scholar 

  8. Manabe K, Amino M (2002) Effects of process parameters and material properties on deformation process in tube hydroforming. J Mater Process Technol 123:285–291

    Article  Google Scholar 

  9. Hwang YM, Lin TC, Chang WC (2007) Experiment on T-shape hydroforming with counter punch. J Mater Process Technol 192–193:243–248

    Article  Google Scholar 

  10. Yuan S, Yuan W, Wang X (2006) Effect of wrinkling behavior on formability and thickness distribution in tube hydroforming. J Mater Process Technol 177:668–671

    Article  Google Scholar 

  11. Aue-U-Lan Y, Ngaile G, Altan T (2004) Optimizing tube hydroforming using process simulation and experimental verification. J Mater Process Technol 146:137–143

    Article  Google Scholar 

  12. Yang JB, Heon BH, Sl O (2006) Design sensitivity analysis and optimization of the hydroforming process. J Mater Process Technol 113:666–672

    Article  Google Scholar 

  13. Fann KJ, Hsiao PY (2003) Optimization of loading conditions for tube hydroforming. J Mater Process Technol 140:520–524

    Article  Google Scholar 

  14. Li B, Nye TJ, Metzger DR (2006) Multi-objective optimization of forming parameters for tube hydroforming process based on the Taguchi method. Int J Adv Manuf Technol 28:23–30

    Article  Google Scholar 

  15. Ben A, EI-Hami A (2014) Global sensitivity analysis and multi-objective optimisation of loading path in tube hydroforming process based on metamodelling techniques. Int J Adv Manuf Technol 71:753–773

    Article  Google Scholar 

  16. An H, Green DE, Johrendt J (2010) Multi-objective optimization and sensitivity analysis of tube hydroforming. Int J Adv Manuf Technol 50:67–84

    Article  Google Scholar 

  17. An H, Green DE, Johrendt J (2012) A hybrid-constrained MOGA and local search method to optimize the load path for tube hydroforming. Int J Adv Manuf Technol 60:1017–1030

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Kadkhodayan M, Moghadam AE (2012) An investigation of the optimal load paths for the hydroforming of T-shaped tubes. Int J Adv Manuf Technol 61:73–85

    Article  Google Scholar 

  20. Kadkhodayan M, Moghadam AE (2013) Optimization of load paths in X- and Y-shaped hydroforming. Int J Mater Form 6:75–91

    Article  Google Scholar 

  21. Mirzaali M, Seyedkashi SMH, Liaghat GH, Moslemi Naeini H, Shojaee K, Moon YH (2012) Application of simulated annealing method to pressure and force loading optimization in tube hydroforming process. Int J Mech Sci 55:78–84

    Article  Google Scholar 

  22. Mirzaali M, Liaghat GH, Moslemi Naeini H, Seyedkashi SMH, Shojaee K (2011) Optimization of tube hydroforming process using simulated annealing algorithm. Procedia Eng 10:3012–3019

    Article  Google Scholar 

  23. Abedrabbo N, Worswick M, Mayer R, Riemsdijk I (2009) Optimization methods for the tube hydroforming process applied to advanced high-strength steels with experimental verification. J Mater Process Technol 209:110–123

    Article  Google Scholar 

  24. Ben A, Pagnacco E, EI-Hami A (2013) Increasing the stability of T-shape tube hydroforming process under stochastic framework. Int J Adv Manuf Technol 69:1343–1357

    Article  Google Scholar 

  25. Ei-Hami A, Radi B, Cherouat A (2012) Reliability-based design optimization analysis of tube hydroforming process. Simulation 88:1129–37

    Article  Google Scholar 

  26. Di Lorenzo R, Ingarao G, Chinesta F (2009) A gradient-based decomposition approach to optimize pressure path and counter punch action in Y-shaped tube hydroforming operations. Int J Adv Manuf Technol 44:49–60

    Article  Google Scholar 

  27. Ingarao G, Di Lorenzo R, Micari F (2009) Internal pressure counter punch action design in Y-shaped tube hydroforming processes: a multi-objective optimisation approach. Comput Struct 87:591–602

    Article  Google Scholar 

  28. Sun C, Chen G, Lin Z (2005) Determining the optimum variable blank-holder forces using adaptive response surface methodology (ARSM). Int J Adv Manuf Technol 26:23–29

    Article  Google Scholar 

  29. Wang GG, Dong Z, Aitchison P (2001) Adaptive response surface method—a global optimization scheme for approximation-based design problems. J Eng Optim 33:707–734

    Article  Google Scholar 

  30. Wang GG (2003) Adaptive response surface method using inherited Latin hypercube design points. J Mech Des 125(2):210–220

    Article  Google Scholar 

  31. Zhao Z, Han X, Jiang C, Zhou X (2010) A nonlinear interval-based optimization method with local-densifying approximation technique. Struct Multidiscip Optim 42:559–573

    Article  Google Scholar 

  32. Jiang C, Han X, Liu GP (2008) A sequential nonlinear interval number programming method for uncertain structures. Comput Methods Appl Mech Eng 197:4250–65

    Article  MATH  Google Scholar 

  33. Wong SM, Hobbs RE, Onof C (2005) An adaptive response surface method for reliability analysis of structures with multiple loading sequences. Struct Saf 28:287–308

    Article  Google Scholar 

  34. Duprat F, Sellier A (2006) Probabilistic approach to corrosion risk due to carbonation via an adaptive response surface method. Probab Eng Mech 21:207–216

    Article  Google Scholar 

  35. Torii AJ, Lopez RH (2011) Reliability analysis of water distribution networks using the adaptive response surface approach. J Hydraul Eng 138:227–236

    Article  Google Scholar 

  36. Roussoulya N, Petitjeanb F, Salauna M (2013) A new adaptive response surface method for reliability analysis. Probab Eng Mech 32:103–115

    Article  Google Scholar 

  37. ** R, Chen W, Simpson TW (2001) Comparative studies of metamodeling techniques under multiple modeling criteria. Struct Multidiscip Optim 23:1–13

    Article  Google Scholar 

  38. Liu GR (2003) Mesh free methods: moving beyond the finite element method. CRC Press, Boca Raton

    MATH  Google Scholar 

  39. LS-DYNA (2006) Keyword user’s manual, v. 971. Livermore Software Technology Corporation, Livermore

    Google Scholar 

  40. Li K (2010) Optimization of loading paths for hydroforming of T-shaped tube. Dissertation for the Master Degree. Harbin Institute of Technology, China

    Google Scholar 

  41. Imaninejad M, Subhash G, Loukus A (2005) Loading path optimization of tube hydroforming process. Int J Mach Tools Manuf 45:1504–14

    Article  Google Scholar 

  42. Jirathearanat S, Hartl C, Altan T (2004) Hydroforming of Y-shapes-product and process design using FEA simulation and experiments. J Mater Process Technol 146:124–9

    Article  Google Scholar 

  43. Ray P, Mac Donald BJ (2004) Determination of the optimal load path for tube hydroforming processes using a fuzzy load control algorithm and finite element analysis. Finite Elem Anal Des 41:173–92

    Article  Google Scholar 

  44. Li ZF, Gao WS (2009) Applied mathematic statistics. Higher education press, Bei**g

    Google Scholar 

  45. Sun JM, Liang YC (2012) Optimal design of machine. China Machine Press, Bei**g

    Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Automobile Dynamic Simulation, Jilin University, Changchun, 130025, People’s Republic of China

    Tianlun Huang, Xuewei Song & Min Liu

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  1. Tianlun Huang
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  2. Xuewei Song
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  3. Min Liu
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Correspondence to Tianlun Huang.

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Huang, T., Song, X. & Liu, M. The optimization of the loading path for T-shape tube hydroforming using adaptive radial basis function. Int J Adv Manuf Technol 82, 1843–1857 (2016). https://doi.org/10.1007/s00170-015-7534-z

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  • DOI: https://doi.org/10.1007/s00170-015-7534-z

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