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

Yang, Xiaoguang; Behroozi, Mohammad; Olatunbosun, Oluremi

doi:10.4236/jilsa.2014.61002

Cited by 16 publications

(10 citation statements)

References 16 publications

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“…The contact pressure pattern generated agrees with that of the literature [36][37][38]. During cornering, the dual-chambers demonstrated a marginal reduction in the lateral force which may undermine from the tire's grip and stability a little.…”

Section: Fe Model Validationsupporting

confidence: 86%

Multi-Chamber Tire Concept for Low Rolling-Resistance

Aldhufairi

Essa

Olatunbosun

2019

SAE Int. J. Passeng. Cars - Mech. Syst.

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Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. • Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of 'fair dealing' under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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Section: Fe Model Validationsupporting

confidence: 86%

Multi-Chamber Tire Concept for Low Rolling-Resistance

Aldhufairi

Essa

Olatunbosun

2019

SAE Int. J. Passeng. Cars - Mech. Syst.

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“…It has been concluded that ANNs can be used as a very powerful and simple alternative technique for the prediction of an optimum cure time to equivalent cure concept, which is traditionally used in the rubber and tire industries. In the work [41], the effective and efficient ANN architecture was found to optimize tire design before an expensive finite element analysis used to confirm the predicted tire performance. Generally speaking, there are few publications about the use of ANNs in the area of polymeric materials or rubbers up to now.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Tensile Response of Carbon Black Filled Rubber Blends by Artificial Neural Network

et al. 2018

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The precise experimental estimation of mechanical properties of rubber blends can be a very costly and time-consuming process. The present work explores the possibilities of increasing its efficiency by using artificial neural networks to study the mechanical behavior of these widely used materials. A multilayer feed-forward back-propagation artificial neural network model, with a strain and the carbon black content as input parameters and stress as an output parameter, has been developed to predict the uniaxial tensile response of vulcanized natural rubber blends with different contents of carbon black in the form of engineering stress-strain curves. A novel procedure has been created for the simulation of the optimized artificial neural network model with input datasets generated by a regression model of an experimental dependence of tensile strain-at-break on the carbon black content in the investigated blends. Errors of the prediction of experimental stress-strain curves, as well as of tensile strain-at-break, tensile stress-at-break and M100 tensile modulus were estimated for all simulated stress-strain curves. The present study demonstrated that the performance of a developed neural network model to predict the stress-strain curves of rubber blends with different contents of carbon black is also exceptionally high in the case of a network that had never learned the input data, which makes it a suitable tool for extensive use in practice.

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“…For traffic information prediction, between numerous nonparametric prediction approaches most often are used artificial neural networks (ANN), support vector regression (SVR), and the adaptive neurofuzzy system (ANFIS)) [15][16][17][18]. The main advantage of the artificial neural network (ANN) is its ability to model very complex multivariable systems, and the quality of prediction is tuned and improved by parameters of the network such as the number of hidden neurons and learning factor [19]. The neural network prediction of the speed profile in transport systems is presented in [15][16].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Automaton Based Vehicle Platoon Modelling and Cooperation Behaviour Profile Prediction

Banjanović-Mehmedović

Butigan²,

Mehmedović³

et al. 2018

Teh. vjesn.

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Autonomous cooperative driving systems require the integration of research activities in the field of embedded systems, robotics, communication, control and artificial intelligence in order to create a secure and intelligent autonomous drivers behaviour patterns in the traffic. Beside autonomous vehicle management, an important research focus is on the cooperation behaviour management. In this paper, we propose hybrid automaton modelling to emulate flexible vehicle Platoon and vehicles cooperation interactions. We introduce novel coding function for Platoon cooperation behaviour profile generation in time, which depends of vehicles number in Platoon and behaviour types. As the behaviour prediction of transportation systems, one of the primarily used methods of artificial intelligence in Intelligent Transport Systems, we propose an approach towards NARX neural network prediction of Platoon cooperation behaviour profile. With incorporation of Platoon manoeuvres dynamic prediction, which is capable of analysing traffic behaviour, this approach would be useful for secure implementation of real autonomous vehicles cooperation.

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

Cited by 16 publications

References 16 publications

Multi-Chamber Tire Concept for Low Rolling-Resistance

Multi-Chamber Tire Concept for Low Rolling-Resistance

Prediction of the Tensile Response of Carbon Black Filled Rubber Blends by Artificial Neural Network

Hybrid Automaton Based Vehicle Platoon Modelling and Cooperation Behaviour Profile Prediction

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