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.