Neural Network Based Constitutive Model for Rubber
Dagli, C. H. and Buczak, A. L. and Ghosh, J. and Embrechts, M. and Ersoy, O. and Kercel, S.
Rubber hyperelasticity is characterized by a strain energy function. To determine the constants in the strain energy function, curve fitting of rubber test data is required. A review of the available strain energy functions shows that it requires much effort to obtain a curve fitting with good accuracy. To overcome this problem, a novel method of defining rubber strain energy function by Feedforward Backpropagation Neural Network is presented. Curve fitting results are given to show the effectiveness and accuracy of the neural network approach. A material model based on the neural network approach is implemented and applied to the simulation of V-ribbed belt tracking using the commercial finite element code-ABAQUS.
Y. Shen et al., "Neural Network Based Constitutive Model for Rubber," Intelligent Engineering Systems Through Artificial Neural Networks, American Society of Mechanical Engineers (ASME), Jan 2002.
Artificial Neural Networks in Engineering Conference: Smart Engineering System Design
Mechanical and Aerospace Engineering
Keywords and Phrases
Backpropogation; Computer Simulation; Curve Fitting; Elasticity; Finite Element Method; Rubber; Strain
Article - Conference proceedings
© 2002 American Society of Mechanical Engineers (ASME), All rights reserved.
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