Multi-objective optimum design of TBR tire structure for enhancing the durability using genetic algorithm

Abstract

This paper is concerned with the multi-objective optimization of the structure of TBR (Truck and bus radial) tire by making use of Genetic algorithm (GA) and Artificial neural network (ANN) in order to effectively enhance the tire durability. Four different types of continuous and discrete design variables are chosen by the carcass path, width and angle of tread belts and the rubber modulus of sidewall and base strip, while the objective functions are defined by the peak strain energy at the belt edge and the peak shear strain of carcass. The approximate models of two objective functions are approximated by neural network, and mathematical sensitivity analysis is substituted with the iterative genetic evolution to deal with the discontinuous discrete-type design variables. The weights of two objective functions are traded-off by adjusting the aspiration levels with respect to the ideal levels. The validity of proposed multi-objective optimization method is illustrated through the numerical experiment.