Contact Stress Prediction Model for Variable Hyperbolic Circular Arc Gear Based on the Optimized Kriging-Response Surface Model

Abstract

In order to study the influence of design parameters (pressure angle, tooth width, tooth line radius, modulus, and moment) on contact stress of variable hyperbolic circular arc gear (VHCAG) and to obtain the best manufacturing parameters, The Kriging-Response Surface Model, a hybrid surrogate model with adaptive quantum particle swarm optimization (QPSO) algorithm was proposed to establish the expression prediction model for the relation between design parameters and contact stress. An intelligent quantum particle swarm optimization algorithm based on adaptive weight and natural selection is proposed to optimize the parameters of Gaussian variation function of the kriging surrogate model to improve its fitting accuracy. The global search ability of quantum particles is improved, and the accuracy and stability of the algorithm are improved by adjusting the weight of quantum particles adaptively and by optimizing the elimination iteration process, and the response relationship between design parameters and contact stress was established. The binomial response surface model of gear design parameters and contact stress is established based on the output obtained through the improved kriging model; this simplifies the complex expression of the kriging model. The effects of parameters and their cross-terms on contact stress are analysed based on the contact stress prediction model established by using the optimized Kriging-Response Surface Model hybrid surrogate model. The hybrid Kriging-Response Surface Model surrogate model lays a foundation for the research on the reliability and robust optimization of cylindrical gears with variable hyperbolic arc tooth profile.