Prediction of Forming Pressure Curve for Hydroforming Processes Using Artificial Neural Network

Abstract

In a hydroforming process, forming pressure versus punch stroke is critical to product quality, and thus the operating pressure curve is strictly controlled according to a preplanned control method. The conventional method of determining such a curve is trial and error, which demands tremendous effort and high cost. In this paper, a neural network approach is proposed to replace the conventional method. It automatically generates the appropriate operating curve whenever any changes in part material properties and forming geometries occur. The network maps non-linear relationships between part geometric variables and forming pressure throughout the punch stroke. The performance of the trained network was tested for various drawing ratios and punch shapes which had not been used for training. The results show that the proposed neural network approach yields products of uniform thickness, thus exhibiting the ability to design an operating pressure curve necessary for guaranteeing good product quality.

ANN Modelling to Optimize Manufacturing Process

“…This complexity makes it difficult to relate the input operating variables to the product quality such as geometry accuracy and geometry surface smoothness. These processes have been implemented and optimized with the use of artificial intelligence with the use of multilayer perceptron which is found to be the most popular network and tries to model the process dynamics and based on this to predict the part quality [3][4][5][6][7][8][9].…”

“…5 Maximum Slope of Heating Curve of thermal profiles evaluated on the surface of joints along the advancing side. 6 Vickers microhardness values measured in the HAZ. 7 Vickers microhardness normalized values measured in the HAZ.…”