Simulation and modelling of sheet metal forming process are well common today in different industries (automotive, aerospace) and several research centers regarding its huge impact for both on production and reliability of the lifecycle of the equipment, and the quality of the product. However, to obtain the best configuration possible with the inputs parameters to achieve high level of production and increasing the durability of the tools needs some extra methods for the optimization for this problem using mostly finite element method cooperated with iterative algorithms based on Artificial Neural Network (ANN) [1]. Whereas this research is focused on modelling of stamping process of stainless steel AISI 304 to investigate to formability of the material, and studying the influence of the friction factor on the quality of the product as well the energy required for each set configuration.

Simulation and modelling of sheet metal forming process are well common today in different industries (automotive, aerospace) and several research centers regarding its huge impact for both on production and reliability of the lifecycle of the equipment, and the quality of the product. However, to obtain the best configuration possible with the inputs parameters to achieve high level of production and increasing the durability of the tools needs some extra methods for the optimization for this problem using mostly finite element method cooperated with iterative algorithms based on Artificial Neural Network (ANN) [1]. Whereas this research is focused on modelling of stamping process of stainless steel AISI 304 to investigate to formability of the material, and studying the influence of the friction factor on the quality of the product as well the energy required for each set configuration.