Optimization of Stamping Process

Stamping is the process of placing flat sheet metal in either blank or coil form into a stamping press where a tool and die surface forms the metal into a net shape. (source: wikipedia). Sheet metal forming is widely used in several industries as it provides a scalable way of manufacturing metal parts, for instance in automotive and aeronautics.


As any industrial process, optimization means both improvement in costs and quality. Costs are directly linked to the speed of the process, and quality is linked to different parameters: springback and rupture in particular.


In order to optimize the process, numerical simulation is a great asset to investigate changes in parameters (speed of the press, thickness of the metal sheet, ...) in a predictive manner. It does not fully replace physical testing, but speed up the process and helps in characterizing the materials properties.

Now, what would be the role of Scilab in all of this? As for other Finite Element Simulation, Scilab proves usefull to set up a numerical workflow for the sake of optimization:


The types of results that we can consider are minimization of thinning (indicators of potential rupture). Those data can easily be extracted from the finite element simulation results, through the support of the ESI Results Files (ERF) in Scilab.

Once those data are extracted from the stamping simulation for different runs (here 10) over a certain design of experiment (varying certain input parameters), we can represent min and max thresholds for a robustness criteria (here the thinning):