Initial Blank Optimization in Multilayer Deep Drawing Process Using GONNS

Abstract

The initial blank in the deep drawing process has a simple shape. After drawing, its perimeter shape becomes very complex. If the initial blank shape is designed in such a way that it is formed into the desired shape after the drawing process, not only does it reduces the time of trimming process but it also decreases the raw material needed substantially. In this paper, the genetically optimized neural network system (GONNS) is proposed as a tool to predict the initial blank shape for the desired final shape. Artificial neural networks (ANNs) represent the final blank shape after a training process and genetic algorithms find the optimum initial blank. The finite element method is employed for simulating the multilayer plate deep drawing process to provide training data for ANN. The GONNS results were verified through experiment in which the error was found to be about 0.2 mm. At last, variations of deformation force, thickness distribution, and thickness strain distribution were investigated using optimum blank. The results show 12% reduction in deformation force and more uniform thickness distribution as well as more consistent thickness strain distribution in the optimum blank shape.