Application of neural network for quality improvement in metal blanking process

The blanking process and the structure of the blanked surface are influenced by both the tooling (clearance and the tool geometry) and the properties of the workpiece material (blank thickness, mechanical properties, microstructure, etc.). Therefore, for a given material, the clearance and tool geometry are the most important parameters. The objective of the present work is to develop a methodology to obtain the optimum punch-die clearance for a given sheet material by the simulation of the blanking process. A damage model of type Lemaitre is used in order to describe crack initiation and propagation into the sheet. The proposed approach combines predictive finite element and neural network modeling of the leading blanking parameters. The numerical results obtained by finite element computation including damage and fracture modeling were utilized to train the developed simulation environment based on back propagation neural network modeling. The comparative study between the numerical results and the experimental ones, shows the good agreement.