A. J. Barber, T. E. Renner, S. You, G. S. Sandlass and A. Maki, “Predicting Tyre Handling Performance Using Neural Network Models,” 2004, SAE Paper No. 2004-011574.

has been cited by the following article:

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

  AUTHORS: Xiaoguang Yang, Mohammad Behroozi, Oluremi A. Olatunbosun

  KEYWORDS: Design Parameters; Finite Element Modelling; Neural Network; Tyre Configuration

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.6 No.1, February 14, 2014

  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.