Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
Geometrical instabilities like wrinkling and necking are 2 major defects in deep drawing process. Because of them, drawability is greatly reduced leading to huge lose of material and money. Friction has an important bearing on wrinkling and necking. Hence their prediction is of utmost importance in deep drawing process design. In past such prediction were made via trial and error approaches based on shop floor experiences. But such approaches are crude and time consuming. To overcome these difficulties, Artificial Neural Network (ANN) has been used in this study. Neural networks are trained based on finite element simulated data. Limiting strain hardening exponent for the success of deep drawing, are arrived at from FE simulations. It has been shown that proposed approach is powerful and fast in predictions of geometrical instabilities in deep drawing process.
How to cite this article:
K.K. Pathak , Vikas Kumar Anand and Geeta Agnihotri . Prediction of Geometrical Instabilities in Deep Drawing Using Artificial Neural Network.
DOI: https://doi.org/10.36478/jeasci.2008.344.349
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2008.344.349