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ISRN Materials Science
Volume 2013 (2013), Article ID 147086, 10 pages
http://dx.doi.org/10.1155/2013/147086
Research Article

Development of a Neural Network Simulator for Studying the Constitutive Behavior of Structural Composite Materials

1Department of Computer Science, Iowa State University, Ames, IA 50011, USA
2Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, USA
3Department of Aerospace Engineering, Iowa State University, Ames, IA 50011, USA
4Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA

Received 5 December 2012; Accepted 9 January 2013

Academic Editors: M. Afzaal, F. Ein-Mozaffari, H. Hermann, F. M. Labajos, and H. Yoshihara

Copyright © 2013 Hyuntae Na 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.

Abstract

This paper introduces a recent development and application of a noncommercial artificial neural network (ANN) simulator with graphical user interface (GUI) to assist in rapid data modeling and analysis in the engineering diffraction field. The real-time network training/simulation monitoring tool has been customized for the study of constitutive behavior of engineering materials, and it has improved data mining and forecasting capabilities of neural networks. This software has been used to train and simulate the finite element modeling (FEM) data for a fiber composite system, both forward and inverse. The forward neural network simulation precisely reduplicates FEM results several orders of magnitude faster than the slow original FEM. The inverse simulation is more challenging; yet, material parameters can be meaningfully determined with the aid of parameter sensitivity information. The simulator GUI also reveals that output node size for materials parameter and input normalization method for strain data are critical train conditions in inverse network. The successful use of ANN modeling and simulator GUI has been validated through engineering neutron diffraction experimental data by determining constitutive laws of the real fiber composite materials via a mathematically rigorous and physically meaningful parameter search process, once the networks are successfully trained from the FEM database.