Kashtiban - MSC.Student Amirkabir university of technology Tehran,Iran
Mollaei - Associate Professor Amirkabir university of technology Tehran,Iran
Ghaffari Tari - MSC.Student Amirkabir university of technology Tehran,Iran

In this paper, the modeling of deep-drawing process using neural networks is established. The relationships between process parameters (punch radius, matrix radius, blank holder force) and part quality (wall thickness) are created, based on a neural network. Finite element analyses are conducted for combination of process parameters designed using statistical full factorial experimental design. A predictive model for wall thickness is created using Levenberg-Marquardt (LM) artificial neural network exploiting finite element analysis results. The results obtained are found to correlate well with experimental data.

sheet metal forming, full factorial, Finite element method, Artificial neural network