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

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 FEMmore » database.« less
Authors:
 [1] ;  [2] ;  [2] ;  [3] ;  [4] ;  [4]
  1. Department of Computer Science, Iowa State University, Ames, IA 50011, USA
  2. Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, USA
  3. Department of Aerospace Engineering, Iowa State University, Ames, IA 50011, USA
  4. Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA
Publication Date:
OSTI Identifier:
1227739
Grant/Contract Number:
AC52-06NA25396
Type:
Published Article
Journal Name:
ISRN Materials Science
Additional Journal Information:
Journal Volume: 2013; Related Information: CHORUS Timestamp: 2016-08-18 09:04:15; Journal ID: ISSN 2090-6099
Publisher:
Hindawi Publishing Corporation
Sponsoring Org:
USDOE
Country of Publication:
Country unknown/Code not available
Language:
English