High strength steels (HSSs) are one of the light-weight sheet metals well suited for reducing vehicle weight due to their higher strength-to-weight ratio. However, HSS tend to have bigger variations in their mechanical properties due to more complex rolling techniques involved in the steel-making process. Such uncertainties, when combined with variations in the process parameters such as friction and blank holder force, pose a significant challenge in maintaining the robustness of HSS sheet metal stamping. The paper presents a systematic and robust approach, combining the power of the finite element method and stochastic statistics to decrease the sensitivity of HSS stamping in the presence of above-mentioned uncertainties. First, the statistical distribution of sheet metal properties of selected HSS is characterized from a material sampling database. Then a separate interval adaptive response surface methodology (RSM) is applied in modeling sheet metal stamping. The new method significantly improves the model accuracy when compared with the conventional RSM within a single interval. Finally, the Monte Carlo method is employed to simulate the stochastic response of material/process variations to stamping quality and to provide optimal process parameter designs to reduce the sensitivity of these effects. The experiment with the obtained optimal process design demonstrates the improvements of stamping robustness using small-batch experiments.
Some improvements on the one-step inverse isogeometric analysis by proposing a multi-step inverse isogeometric methodology in sheet metal stamping processes