Optimization method for stamping tools under reliability constraints using genetic algorithms and finite element simulations

Controlling variability and process optimization are major issues of manufacturing processes which should be tackled together since optimal processes must be robust. There is a lack of numerical tool combining optimization and robustness. In this paper, a complete approach starting from modelling and leading to the selection of robust optimal process parameters is proposed. A model of stamping part is developed through Finite Element simulation codes and validated by experimental methods. The search for optimal tool configurations is performed by optimizing a desirability function and by means of a genetic algorithm based optimization code. Several tool configurations are selected from the resulting solutions and are observed through robustness analysis. Noise parameters relating to friction and material mechanical properties are taken into consideration during this analysis. A quadratic response surface developed with design of experiments (DOE) links noise parameters to geometrical variations of parts. For every optimal configuration, the rate of non-conform parts which do not satisfy the design requirements is assessed and the more robust tool configuration is selected. Finally, a sensitivity analysis is performed on this ultimate configuration to observe the respective influence of noise parameters on the process scattering. The method has been applied on a U-shape part.

[1]  Syed H. Masood,et al.  Tool wear prediction on sheet metal forming die of automotive part based on numerical simulation method , 2007 .

[2]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[3]  Kwang-Jae Kim,et al.  Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions , 2000 .

[4]  Genichi Taguchi,et al.  Taguchi on Robust Technology Development: Bringing Quality Engineering Upstream , 1992 .

[5]  Harald Niederreiter,et al.  Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.

[6]  Bernard Rolfe,et al.  Multivariate modelling of variability in sheet metal forming , 2008 .

[7]  R. H. Wagoner,et al.  Variability of sheet formability and formability testing , 2002 .

[8]  Karl Kuzman,et al.  Optimization of stamping processes aiming at maximal process stability , 2005 .

[9]  Karl D. Majeske,et al.  Identifying Sources of Variation in Sheet Metal Stamping , 2003 .

[10]  A. M. Prior,et al.  Applications of implicit and explicit finite element techniques to metal forming , 1994 .

[11]  F. Guerin,et al.  Numerical Evaluation of the Tool Wear Influence on Metal-Punching Processes , 2003 .

[12]  Karl Kuzman,et al.  Sensitivity and stability evaluation of the deep drawing process , 2002 .

[13]  Zoltán Koczor,et al.  Handling Contradicting Requirements Using Desirability Functions , 2004 .

[14]  Yann Ledoux,et al.  Impact Of The Material Variability On The Stamping Process: Numerical And Analytical Analysis , 2007 .

[15]  R. Hill.  A theory of the yielding and plastic flow of anisotropic metals , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[16]  George E. P. Box,et al.  Empirical Model‐Building and Response Surfaces , 1988 .

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[18]  Tomas Jansson,et al.  Reliability analysis of a sheet metal forming process using Monte Carlo analysis and metamodels , 2008 .