汽车轮胎微观和宏观行为预测的神经网络方法

Xiaoguang Yang, M. Behroozi, O. Olatunbosun
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引用次数: 20

摘要

有限元分析由于能够反映轮胎胎体的详细分层结构,已成为轮胎行业进行轮胎虚拟开发的首选工具。然而,在轮胎设计和开发中应用有限元分析仍然是非常耗时和昂贵的。在这里,评估各种人工神经网络(ANN)体系结构在预测轮胎性能方面的应用,以选择最有效和最高效的体系结构,从而允许以低成本进行广泛的参数研究,并在使用更昂贵的完整有限元分析来确认预测性能之前优化轮胎设计。
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A Neural Network Approach to Predicting Car Tyre Micro-Scale and Macro-Scale Behaviour
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.
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