Numerical analyses of tube hydroforming problem using artificial neural networks

The hydroforming process is characterized in these recent years by the remarkable development to compare with other processes manufacturing such as deep drawing and bending... Hydroforming is a reliable process that improves the resistance and rigidity of parts with the geometrical and dimensional tolerances allowing for lower costs tool and therefore an overall cost of manufacturing reduced. The success of hydroforming process requires the control simultaneously of various parameters such as used material properties, thickness, internal pressure,... In this paper, we introduce our model based in a neural approach (ANN) compared to the numerical simulation and experimental results. This method allows a better thickness distribution during Tee extrusion tube hydroforming process (THF) and the optimization of the final part geometry. A multilayer’s neural networks (MNN) program is used to control the tube wall thickness variation, so the loading paths (axial feeding and the internal pressure) are used like inputs for our networks, the thickness is the output.