- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
ISRN Materials Science
Volume 2013 (2013), Article ID 147086, 10 pages
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.
- R. Hill, “The essential structure of constitutive laws for metal composites and polycrystals,” Journal of the Mechanics and Physics of Solids, vol. 15, no. 2, pp. 79–95, 1967.
- M. A. M. Bourke, D. C. Dunand, and E. Ustundag, “SMARTS—a spectrometer for strain measurement in engineering materials,” Applied Physics A, vol. 74, pp. S1707–S1709, 2002.
- X. L. Wang, T. M. Holden, G. Q. Rennich et al., “VULCAN—the engineering diffractometer at the SNS,” Physica B, vol. 385-386, pp. 673–675, 2006.
- C. N. Tomé, “Self-consistent polycrystal models: a directional compliance criterion to describe grain interactions,” Modelling and Simulation in Materials Science and Engineering, vol. 7, no. 5, pp. 723–738, 1999.
- J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proceedings of the National Academy of Sciences of the United States of America, vol. 79, no. 8, pp. 2554–2558, 1982.
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.
- I. Aleksander and H. Morton, An Introduction to Neural Computing, Van Nostrand Reinhold Co., New York, NY, USA, 1990.
- C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK, 1995.
- K. Swingler, Applying Neural Networks: A Practical Guide, Academic Press, 1996.
- H. Ceylan, Analysis and Design of Concrete Pavement Systems Using Artificial Neural Networks, University of Illinois at Urbana-Champaign, Urbana, Ill, USA, 2002.
- B. Clausen, S. Y. Lee, E. Üstündag, C. C. Aydiner, R. D. Conner, and M. A. M. Bourke, “Compressive yielding of tungsten fiber reinforced bulk metallic glass composites,” Scripta Materialia, vol. 49, no. 2, pp. 123–128, 2003.
- B. Denizer, E. Ustundag, H. Ceylan, L. Li, and S. Y. Lee, “Engineering neutron diffraction data analysis with inverse neural network modeling,” Materials Science Forum. In press.
- S. L. Ross, “Parametric investigation of artificial neural network development to study composites,” unpublished work.
- B. Denizer, Artificial Neural Network Analysis of the Mechanical Properties of Tungsten Fiber/Bulk Metallic Glass Matrix Composites via Neutron Diffraction and Finite Element Modeling, Iowa State University, Ames, Iowa, USA, 2008.
- D. Dragoi, E. Üstündag, B. Clausen, and M. A. M. Bourke, “Investigation of thermal residual stresses in tungsten-fiber/bulk metallic glass matrix composites,” Scripta Materialia, vol. 45, no. 2, pp. 245–252, 2001.