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Optimization methods for complex sheet metal stamping computer aided engineering

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Abstract

Nowadays, sheet metal stamping processes design is not a trivial task due to the complex issues to be taken into account (complex shapes forming, conflicting design goals and so on). Therefore, proper design methodologies to reduce times and costs have to be developed mostly based on computer aided procedures. In this paper, a computer aided approach is proposed with the aim to offer a methodology able to solve very complex sheet metal stamping processes, in particular a progressive design approach based on the integration between numerical simulations and optimization methodologies is presented. In particular, Response Surface Method, Moving Least Squares approximation and Pareto optimal solutions search techniques were applied in order to design two different complex 3D stamping operations. The proposed design procedure is able to verify the necessity of a spatially differentiated restraining forces approach and to design the best policy for them. In particular, different part “quality” indicators were monitored such as springback occurrence and thinning. An explicit/forming-implicit/springback approach was utilized to develop the numerical simulations. To sum up, a new and flexible design methodology is proposed, able to: deal with complex sheet metal stamping processes; investigate many possible technological scenarios; carry out a set of reliable solutions able to satisfy different design requirements; offer different optimization possibilities in order to take in to account all the sheet metal stamping design issues.

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References

  • Belytschko T, Krongauz Y, Organ D, Fleming M, Krysl P (1996) Meshless method: an overview and recent development. Comput Met Appl Mech Eng 139:3–47

    Article  MATH  Google Scholar 

  • Breitkopf P, Rassineux A, Savignat PJM (2004) Integration constraint in diffuse element method. Comput Methods Appl Mech Eng 193:1203–1220

    Article  MATH  Google Scholar 

  • Breitkopf P, 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 

  • Castro C, Antonio C, Sousa L (2004) Optimisation of shape and process parameters in metal forging using genetic algorithms. J Mater Process Technol 146:356–364

    Article  Google Scholar 

  • Cheng HS, Cao J, Xia ZC (2007) An accelerated springback compensation method. Int J Mech Sci 49:267–279

    Article  Google Scholar 

  • Das I, Dennis JE (1997) A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multi-criteria optimization problems. Struct Optim 14:63–69

    Article  Google Scholar 

  • Deb K (2001) Multiobjective optimization using evolutionary algorithms. Wiley, New York

    Google Scholar 

  • Di Lorenzo R, Ingarao G, Chinesta 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 44:44–60

    Article  Google Scholar 

  • Di Lorenzo R, Ingarao G, Chinesta F (2010) Integration of gradient based and response surface methods to develop a cascade optimization strategy for y-shaped tube hydroforming process design. Adv Eng Softw 41:336–348

    Article  MATH  Google Scholar 

  • Donglai W, Zhenshan C, Jun C (2008) Optimization and tolerance prediction of sheet metal forming process using response surface model. Comput Mater Sci 42:228–233

    Article  Google Scholar 

  • Fann K, Hsiao P (2003) Optimization of loading conditions for tube hydroforming. J Mater Process Technol 140:520–524

    Article  Google Scholar 

  • Fourment L, Do T, Habbal A, Bouzaiane (2005) A Gradient non gradient and hybrid algorithms for optimizing 2D and 3D forging sequences. In: Proceeding of ESAFORM, Cluj-Napoca, Romania

    Google Scholar 

  • Fratini L, Ingarao G, Micari F (2008) On the springback prediction in 3d sheet metal forming processes. Steel Res International 79:77–83

    Google Scholar 

  • Geng L, Wagoner RH (2004a) Die design method for sheet springback. Int J Mech Sci 66:1097–2003

    Google Scholar 

  • Geng R, Wagoner RH (2004b) Role of plastic anisotropy and its evolution on springback. Int J Mech Sci 44:123–128

    Article  Google Scholar 

  • Hu W, Enying L, Li GY, Zhong ZH (2008) Optimization of sheet metal forming processes by the use of space mapping based meta-modelling method. Int J Adv Manuf Technol 39:642–655

    Article  Google Scholar 

  • Ingarao G, Di Lorenzo R, Micari F (2009a) Analysis of stamping performances of dual phase steels: a multi-objective approach to reduce springback and thinning failure. Mater Des 30:4421–4433

    Google Scholar 

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

    Article  Google Scholar 

  • Jansson T, Andersson A, Nilsson L (2005a) Optimization of draw-in for an automotive sheet metal part. An evaluation using surrogate models and response surfaces. J Mater Process Technol 159:426–434

    Article  Google Scholar 

  • Jansson T, Andersson A, Nilsson L (2005b) Optimization of draw-in for an automotive sheet metal part—an evaluation using surrogate models and response surfaces. J Mater Process Technol 159:234–426

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Jin R, Chen W, Simpson TW (2001) Comparative studies of meta-modelling techniques under multiple modelling criteria. Struct Multidisc Optim 23:1–13

    Article  Google Scholar 

  • Kazan R, Firat M, Tiryaki AE (2009) Prediction of springback in wipe-bending process of sheet metal using neural network. Mater Des 30:418–423

    Google Scholar 

  • Kleinermann J, Ponthot J (2003) Parameter identification and shape/ process optimization in metal forming simulation. J Mater Process Technol 139:521–526

    Article  Google Scholar 

  • Li K, Carden WP, Wagoner RH (2002) Simulation of springback. Int J Mech Sci 44:103–112

    Article  MATH  Google Scholar 

  • Lin Z, Wang W, Chen G (2007) A new strategy to optimize variable blank holder force towards improving the forming limits of aluminium sheet metal forming. J Mater Process Technol 183:339–346

    Article  Google Scholar 

  • Liu GP, Han X, Jiang C (2008) Novel multi-objective optimization method based on an approximation model management technique. Comput Methods Appl Mech Eng 197:2719–2731

    Article  Google Scholar 

  • Meinders T, Burchitza IA, Bonte MHA, Lingbeek RA (2008) Numerical product design: springback prediction, compensation and optimization. Int J Mach Tools Manuf 48:499–514

    Article  Google Scholar 

  • Messac A, Mattson CA (2002) Generating well-distributed sets of Pareto points for engineering design using physical programming. Optim Engineering 3:431–450

    Article  MATH  Google Scholar 

  • Myers RH, Montgomery DC (2002) Response surface methodology process and product optimization using designed experiments, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  • Naceur HA, Batoz J, Guo Y, Knopf-Lenoir C (2004) Optimization of draw bead restraining forces and draw bead design in sheet metal forming process. J Mater Process Technol 146:250–262

    Article  Google Scholar 

  • Naceur H, Guo YQ, Ben-Elechi S (2006) Response surface methodology for design of sheet forming parameters to control springback effects. Comput Struct 84:1651–1663

    Article  Google Scholar 

  • Naceur H, Ben-Elechi S, Batoz JL, Knopf-Lenoir C (2008) Response surface methodology for the rapid design of aluminium sheet metal forming parameters. Mater Des 29:781–790

    Google Scholar 

  • Oudjene M, Ben-Ayed L, Delameziere A, Batoz JL (2009) Shape optimisation of clinching tools using the response surface methodology with moving least-square approximation. J Mater Process Technol 209:289–296

    Article  Google Scholar 

  • Pepelux L, Ponthot JP (2002) Finite element simulation of springback in sheet metal forming. J Mater Process Technol 125:785–791

    Article  Google Scholar 

  • Rassineux A, Breitkopf P, Villon P (2003) Simultaneous surface and tetrahedron mesh adaptation using mesh free techniques. Int J Numer Methods Eng 57:371–389

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  • Sheng Z, Jirathearanat S, Altan T (2004) Adaptive FEM simulation for prediction of variable blank holder force in conical cup drawing. Int J Mach Tools Manuf 44:487–494

    Article  Google Scholar 

  • Shivpuri R, Zhang W (2009) Robust design of spatially distributed friction for reduced wrinkling and thinning failure in sheet drawing. Mater Des 30:2043–2055

    Google Scholar 

  • Simpson TW, Peplinski JD, Koch PN, Allen JK (2001) Meta-models for computer-based engineering design: survey and recommendations. Eng Comput 17:129–150

    Article  MATH  Google Scholar 

  • Uthaisangsuk V, Prahl U, Bleck W (2007) Stress based failure criterion for formability characterisation of metastable steels. Comput Mater Sci 39:43–48

    Article  Google Scholar 

  • Wagoner RH, Li M (2007) Simulation of springback: through-thickness integration. Int J Plast 23:345–360

    Article  MATH  Google Scholar 

  • Wang L, Lee TC (2005) Controlled strain path forming process with space variant blank holder force using RSM method. J Mater Process Technol 167:447–455

    Article  Google Scholar 

  • Wei L, Yuying Y (2007) Multi-objective optimization of an auto panel drawing die face design by mesh morphing. Comput Aided Des 39:863–879

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wei L, Yuying Y, Zhongwen X, Lihong Z (2009) Springback control of sheet metal forming based on the response-surface method and multi-objective genetic algorithm. Mater Sci Eng A 499:325–338

    Article  Google Scholar 

  • Wu PD, Graf A, MacEwen SR, Lloyd DJ, Jain M, Neale KW (2005) On forming limit stress diagram analysis. Int J Solid Struct 42:2225–2241

    Article  Google Scholar 

  • Xu WL, Ma CH, Li CH, Feng WJ (2004) Sensitive factors in springback simulation for sheet metal forming. J Mater Process Technol 151:217–222

    Article  Google Scholar 

  • Zhang W, Shivpuri R (2009) Probabilistic design of aluminium sheet drawing for reduced risk of wrinkling and fracture. Reliab Eng Syst Saf 94:152–161

    Article  Google Scholar 

  • Zhao G, Ma X, Zhao X, Grandhi R (2004) Studies on optimization of metal forming processes using sensitivity analysis methods. J Mater Process Technol 147:217–228

    Article  Google Scholar 

Download references

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Correspondence to Rosa Di Lorenzo.

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Ingarao, G., Di Lorenzo, R. Optimization methods for complex sheet metal stamping computer aided engineering. Struct Multidisc Optim 42, 459–480 (2010). https://doi.org/10.1007/s00158-010-0519-5

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  • DOI: https://doi.org/10.1007/s00158-010-0519-5

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