A Neural Network Approach to Predicting Car Tyre Micro-Scale and Macro-Scale Behaviour

A Neural Network Approach to Predicting Car Tyre Micro-Scale and Macro-Scale Behaviour

FINITE ELEMENT MODEL OF TYRE CROSS-SECTION IN
ABAQUS.

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.

Based on a series of uni-axial tests and evaluations using ABAQUS, the Yeoh hyperelastic model constants for different rubber components were obtained and listed in Table 1.

HYPERELASTIC MATERIAL CONSTANTS

Citations: 

Xiaoguang Yang, Mohammad Behroozi, Oluremi A. Olatunbosun research, Journal of Intelligent Learning Systems and Applications Journal 2014 Vol. 6 by Scientific Research Publishing, DOI: 10.4236/jilsa.2014.61002

Image Credits: scirp.org , Vol.6 No.1