Table of Contents
Advances in Artificial Neural Systems
Volume 2011, Article ID 607374, 8 pages
Research Article

Quasi-Non-Destructive Evaluation of Yield Strength Using Neural Networks

Department of Applied Mechanics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India

Received 26 January 2011; Accepted 17 April 2011

Academic Editor: Ping Feng Pai

Copyright © 2011 G. Partheepan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The objective of this paper is to delineate a method for determining the yield strength of a material in a virtually nondestructive manner. Conventional test methods for predicting the yield strength require the removal of large material samples from the in-service component, which is impractical. In this paper, the power of neural networks in predicting the yield strength from the data obtained by conducting tension test on newly developed dumb-bell-shaped miniature specimen is demonstrated using the self-organizing capabilities of the ANN. The input to the neural network is the breakaway load obtained from the miniature test, and the output obtained from the model is yield strength value. The value of the yield strength estimated by neural network is found to be in good agreement (<5% error) with that of the actual value from the standard test. The neural network models are convenient and powerful tools for practical applications in solving various problems in engineering.