Neural network based modeling and optimization of deep drawing – extrusion combined process

Abstract

A combined deep drawing---extrusion process is modeled with artificial neural networks (ANN's). The process is used for manufacturing synchronizer rings and it combines sheet and bulk metal forming processes. Input---output data relevant to the process was collected. The inputs represent geometrical parameters of the synchronizer ring and the outputs are the total equivalent plastic strain (TEPS), contact ratio and forming force. This data is used to train the ANN which approximates the input-output relation well and therefore can be relied on in predicting the process input parameters that will result in desired outputs provided by the designer. The complex method constrained optimization is applied to the ANN model to find the inputs or geometrical parameters that will produce the desired or optimum values of TEPS, contact ratio and forming force. This information will be very hard to obtain by just looking at the available historical input---output data. Therefore, the presented technique is very useful for selection of process design parameters to obtain desired product properties.