Title: A comparison of different neural network training algorithms for hydromechanical deep drawing
Authors: Swadesh Kumar Singh, D. Ravi Kumar
Addresses: Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi-16, India. ' Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi-16, India
Abstract: In the hydromechanical deep drawing process, a pressure chamber is attached to the drawing die and the cup is drawn into the chamber against the fluid pressure. This process offers several advantages over conventional deep drawing, such as higher drawability, more uniform thickness distribution, better surface finish etc. Optimisation of this process is more difficult because of the large number of variables which interact in a complex way. Artificial Neural Networks (ANN) are being applied to an increasing number of real-world problems of considerable complexity. They offer reliable solutions to a variety of problems (viz. prediction and modelling), where the physical processes are not understood or are highly complex. This paper compares several training algorithms in an attempt to find an ideal artificial neural network-training algorithm to model hydromechanical deep drawing. A comparison was made between ANN trained and experimental results of hydromechanical deep drawing using low carbon extra deep drawing (EDD) grade steel sheets of 0.96mm thickness.
Keywords: artificial neural networks; back propagation; counter pressure; deep drawing; EDD; training; hydromechanical deep drawing.
International Journal of Materials and Product Technology, 2004 Vol.21 No.1/2/3, pp.186 - 199
Published online: 14 Jun 2004 *Full-text access for editors Access for subscribers Purchase this article Comment on this article