Title

Neural Network Based Constitutive Model for Rubber

Editor(s)

Dagli, C. H. and Buczak, A. L. and Ghosh, J. and Embrechts, M. and Ersoy, O. and Kercel, S.

Abstract

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.

Meeting Name

Artificial Neural Networks in Engineering Conference: Smart Engineering System Design

Department(s)

Mechanical and Aerospace Engineering

Keywords and Phrases

Backpropogation; Computer Simulation; Curve Fitting; Elasticity; Finite Element Method; Rubber; Strain

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2002 American Society of Mechanical Engineers (ASME), All rights reserved.

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