Journal Published Online: 01 January 1997
Volume 25, Issue 1

Neural Network Approach for Prediction of Wrinkling Limit in Square Metal Sheet Under Diagonal Tension



A method of predicting the onset of wrinkling in the Yoshida Buckling Test, devised to simulate the wrinkling behavior in press-forming of sheet metal, has been developed in the present work by using an artificial neural network. The influence of different network architectures, learning parameters, and material coefficients has been investigated. The neural network was trained using data obtained by finite element analysis. The effectiveness of a neural network as a tool for predicting wrinkling limits in sheet metal-forming is examined. It is found that the trained neural network is capable of covering a wide range of material properties and its prediction of nominal strain at the onset of wrinkling is in reasonable agreement with the analytical results.

Author Information

Di, S
Monash University, Clayton, Victoria, Australia
Thomson, PF
Monash University, Clayton, Victoria, Australia
Pages: 8
Price: $25.00
Reprints and Permissions
Reprints and copyright permissions can be requested through the
Copyright Clearance Center
Stock #: JTE11327J
ISSN: 0090-3973
DOI: 10.1520/JTE11327J