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A Neural Network Approach to Predicting Car Tyre Micro-Scale and Macro-Scale Behaviour
School of Mechanical Engineering, University of Birmingham, Birmingham, UK.
School of Mechanical Engineering, University of Birmingham, Birmingham, UK.
School of Mechanical Engineering, University of Birmingham, Birmingham, UK.
- 1 School of Mechanical Engineering, University of Birmingham, Birmingham, UK.
- 2 School of Mechanical Engineering, University of Birmingham, Birmingham, UK.
- 3 School of Mechanical Engineering, University of Birmingham, Birmingham, UK.
Journal of Intelligent Learning Systems and Applications·Volume 06 (2014)·Pages 11–20·Published 27 January 2014·DOI10.4236/jilsa.2014.61002
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Abstract
Finite Element (FE) analysis has become the favoured tool in the tyre industry for virtual development of tyres because of the ability to represent the detailed lay - up of the tyre carcass. However, application of FE analysis in tyre design and development is still very time - consuming and expensive. Here, the application of various Artificial Neural Network (ANN) architectures to predicting tyre performance is assessed to select the most effective and efficient architecture, to allow extensive parametric studies to be carried out inexpensively and to optimise tyre design before a much more expensive full FE analysis is used to confirm the predicted performance.
KeywordsDesign ParametersFinite Element ModellingNeural NetworkTyre Configuration
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