Structure optimization of pneumatic tire using an artificial neural network

An application of neural networks to tire optimization designs is presented to alleviate the stress concentration of toe opening. As well known, it is either uncertain or time-consuming to obtain the global optimum solution by using classical local search methods when objective function of optimization is both nonconvex and implicit. In addition, it is infeasible to use local search method based on iteration to optimize tire mechanical property because analysis of tire mechanical responses is involved with material nonlinearity, geometry nonlinearity and boundary nonlinearity. In this paper, a GRNN is constructed to optimize the stress of toe opening by looking at an optimum Young's modulus and cord direction of tire body rubber-cord composite material layer.