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Neural Network Bushing Model Development Using Simulation

[+] Author Affiliations
Jennifer L. Johrendt, Peter R. Frise

University of Windsor, Windsor, ON, Canada

Paper No. DETC2010-28103, pp. 101-109; 9 pages
  • ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 4: 12th International Conference on Advanced Vehicle and Tire Technologies; 4th International Conference on Micro- and Nanosystems
  • Montreal, Quebec, Canada, August 15–18, 2010
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4412-0 | eISBN: 978-0-7918-3881-5
  • Copyright © 2010 by ASME


Neural networks are computationally efficient mathematical models that can be used to model quantitative and qualitative data. A neural network can be created through training with known input and output load-deflection data such that it learns to generalize the material characteristics without over-predicting the training data and losing its ability to anticipate behavior outside the training set. The challenge in creating a neural network model of a rubber bushing in a virtual model of a prototype assembly, for instance, is the lack of a physical prototype assembly. This paper describes a method by which data can be measured from a virtual prototype and used to define an appropriate data acquisition for the physical bushing. Training data can then be acquired using these guidelines and used for neural network model development. Subsequently, the enhanced model can then be used in the virtual simulation environment to increase the accuracy of the simulation results.

Copyright © 2010 by ASME



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