Skip to main content
SpringerLink
Log in

Increasing the stability of T-shape tube hydroforming process under stochastic framework

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Metal forming processes present several sources of uncertainties coming from material properties, geometric characteristics, and loading paths. During the manufacturing phase, such parameters may vary affecting the process stability and increasing the defect parts. Stochastic framework seems more pertinent than classical deterministic approaches to treat such problems since it is intended to include variabilities at the early design stage. In the present work, tube hydroforming process widely used in various industry applications is investigated. To ensure the process stability, loading paths should be optimized with taking into account randomness associated to the input parameters. To control the potential failure modes, the Forming Limit Stress Diagram is implemented in the finite element code to avoid necking while a simple geometrical criterion is defined for wrinkling. A global sensitivity analysis using the variance-based method is done which shows that the selected random parameters impact considerably the variance of failure indicators. Then, a numerical example of T-shape tube hydroforming process is proposed to show the efficiency of the stochastic framework. Statistical and probabilistic observations of the optimum solution show that the stochastic approach yields to an optimum less sensitive to such fluctuations which improves the process stability and minimizes considerably the percentage of defect parts in a mass production environment.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kocanda A, Sadlowska H (2008) Automotive component development by means of hydroforming. Arch Civ Mech Eng 8:55–72

    Article  Google Scholar 

  2. Hartl Ch (2005) Research and advances in fundamentals and industrial applications of hydroforming. J Mater Process Tech 167:383–392

    Article  Google Scholar 

  3. Parsa MH, Darbandi P (2008) Experimental and numerical analyses of sheet hydroforming process for production of an automobile body part. J Mater Process Tech 198:381–390

    Article  Google Scholar 

  4. 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 Tech 159:303–310

    Article  Google Scholar 

  5. Jansson M, Nilsson L, Simonsson K (2007) On process parameter estimation for the tube hydroforming process. J Mater Process Tech 190:1–11

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. 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 Tech 209:110–123

    Article  Google Scholar 

  9. 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 

  10. Alaswad A, Benyounis KY, Olabi AG (2011) Employment of finite element analysis and response surface methodology to investigate the geometrical factors in T-type bi-layered tube hydroforming. Adv Eng Software 42:917–926

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Cherouat A, Saanouni K, Hammi Y (2002) Numerical improvement of thin tubes hydroforming with respect to ductile damage. Int J Mech Sci 44:2427–2446

    Article  MATH  Google Scholar 

  13. 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 41:173–192

    Article  Google Scholar 

  14. Gantar G, Kuzman K (2005) Optimization of stamping processes aiming at maximal process stability. J Mater Process Tech 167:237–243

    Article  Google Scholar 

  15. Karthik V, Comstock RJ, Hershberger DL, Wagoner RH (2002) Variability of sheet formability and formability testing. J Mater Process Tech 121:350–362

    Article  Google Scholar 

  16. Zhang W, Shivpuri R (2009) Probabilistic design of aluminum sheet drawing for reduced risk of wrinkling and fracture. Reliabil Eng Syst Safety 94:152–161

    Article  Google Scholar 

  17. Sun G, Li G, Gong Z, Cui X, Yang X, Li Q (2010) Multiobjective robust optimization method for drawbead design in sheet metal forming. Mater Des 31:1917–1929

    Article  Google Scholar 

  18. Abaqus Version 6.11, analysis user’s manual, vol 4

  19. Panich S, Uthaisangsuk V, Juntaratin J, Suranuntchai S (2011) Determination of forming limit stress diagram for formability prediction of SPCE 270 steel sheet. J Met Mater Min 21:19–27

    Google Scholar 

  20. Koç M, Altan T (2002) Prediction of forming limits and parameters in the tube hydroforming process. Int J Mach Tools Manuf 42:123–138

    Article  Google Scholar 

  21. Strano M, Jirathearanat S, Shiuan-Guang S, Altan T (2004) Virtual process development in tube hydroforming. J Mater Process Tech 146:130–136

    Article  Google Scholar 

  22. Shu-hui L, Bing Y, Wei-gang Z, Zhong-qin L (2008) Loading path prediction for tube hydroforming process using a fuzzy control strategy. Mater Des 29:1110–1116

    Article  Google Scholar 

  23. Chu E, Xu Y (2004) Hydroforming of aluminum extrusion tubes for automotive applications. Part I: buckling, wrinkling and bursting analyses of aluminum tubes. Int J Mech Sci 46:263– 283

    Article  Google Scholar 

  24. Xing HL, Makinouchi A (2001) Numerical analysis and design for tubular hydroforming. Int J Mech Sci 43:1009– 1026

    Article  MATH  Google Scholar 

  25. Hu W, Li Enying, Yao LG (2001) The least square support vector regression coupled with parallel sampling scheme metamodeling technique and application in sheet forming optimization. Mater des 30:1468–1479

    Google Scholar 

  26. Schenk O, Hillmann M (2004) Optimal design of metal forming die surfaces with evolution strategies. Comput Struct 82:1695–1705

    Article  Google Scholar 

  27. Breitkopf S, Naceur H, Rassineux A, Villon P (2005) Moving least squares response surface approximation: formulation and metal forming applications. Comput Struct 83:1411–1428

    Article  Google Scholar 

  28. Ben Abdessalem A, El Hami A, Cherouat A. Optimization of the tube hydroforming process using probabilistic constraints on failure modes Proceedings of the Tenth ICCST, Civil-Comp. Stirlingshire, UK, p 2010

  29. Stoughton TB (2000) A general forming limit criterion for Sheet Metal Forming. Int J Mech Sci 42:1–27

    Article  MATH  Google Scholar 

  30. Saltelli A (2002) Sensitivity analysis for importance assessment. Risk Anal 22:579–590

    Article  Google Scholar 

  31. Sobol IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 55:271–280

    Article  MathSciNet  MATH  Google Scholar 

  32. Patelli E, Pradlearter HJ, Schueller GI (2010) Global sensitivity of structure variability by random sampling. Comput Phys Commun 181:2072–2081

    Article  MATH  Google Scholar 

  33. Saltelli A (2002) Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun 145:280–297

    Article  MATH  Google Scholar 

  34. Diwekar UM (2003) Introduction to applied optimization. Kluwer Academic, MA

    Book  MATH  Google Scholar 

  35. Frangopol DM (1985) Structural optimization using reliability concepts. J Struct Eng (ASCE) 111:2288–2301

    Article  Google Scholar 

  36. Qu X, Hafta RT (2003) Design under uncertainty using Monte Carlo simulation and probabilistic factor Proceedings of ASMEDETC’03 conference

  37. Tu J, Choi KK, Park YH (1999) A new study on reliability-based design optimization. J Mech Des 121:557–565

    Article  Google Scholar 

  38. Cheng GD, Xu L, Jiang L (2006) A sequential approximate programming strategy for reliability-based optimization. Comput Struct 84:1353–1367

    Article  Google Scholar 

  39. Du X, Chen W (2004) Sequential optimization and reliability assessment method for efficient probabilistic design. J Mech Des (ASME) 126:225–233

    Article  Google Scholar 

  40. De Souza T, Rolfe B (2008) Multivariate modelling of variability in sheet metal forming. J Mater Process Tech 203:1–12

    Article  Google Scholar 

  41. MATLAB 6.1 (2000) The MathWorks Inc., Natick, MA

Download references

Author information

Authors and Affiliations

  1. Université de Bordeaux, I2M, UMR CNRS 5295, 351 Cours de la Libération, 33405, Talence Cedex, France

    A. Ben Abdessalem

  2. INSA de Rouen, LOFIMS, B.P. 8, Avenue de l’université, 76801, St Etienne du Rouvray, France

    E. Pagnacco & A. El-Hami

Authors
  1. A. Ben Abdessalem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Pagnacco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. El-Hami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Ben Abdessalem.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ben Abdessalem, A., Pagnacco, E. & El-Hami, A. Increasing the stability of T-shape tube hydroforming process under stochastic framework. Int J Adv Manuf Technol 69, 1343–1357 (2013). https://doi.org/10.1007/s00170-013-5062-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-013-5062-2

Keywords

Search

Navigation