Research On Curvic Couplings Structure Optimization Considering The Effects Of Fretting Fatigue

Curvic couplings are widely used in areo-engine due its advantagrs of the positioning precision,automatic centering,excellent structure stability and strong load bearing ability.As for fretting fatigue could reduce fatigue life,it is considered as hidden danger of aviation.Fretting fatigue exists in the curvic couplings under working condition,it is of great significance to study Curvic Coupling under working condition.A lot of researches on the design of Curvic Couplings have been studied,however,the influences of fretting fatigueare barely taken into consideration,thus the research on the optimization of Curvic Couplings which fretting fatigue is considered in and fretting fatigue life prediction model are required which will be great contribution to the engineering applications.The major works of this paper are represented as follows.First of all,a newfretting fatigue life prediction model based on the study of fretting characteristics of 2D Curvic Couplings was established and the relevances of damage parameters to the fatigue life was studied.Different model was used to predict the fatigue life of specimen.The efficiency of life prediction model was evaluated with the study on the relevance of damage parameters to the fatigue life.The results show that the new model predicted more precise than other prediction models.High-correlation occured between fatigue life with SWT,SSR,SOC,CCBparameters,while low-correlation occured between fatigue life with FS parameter,which was consistent with predicted results.Secondly,a research on the sensibility of the maximum equivalent stress and fretting parameter was studied.The influences of design parameters and loadson the maximum equivalent stress and fretting parameters of Curvic couplings are analyzed.The results indicated that,the maximum equivalent stress and fretting parameters were greatly influenced by coefficient of friction,inside diameter,coefficient of whole depth,tooth pressure angle,transition arc and preload,while the effects of gamble height and torque were less.Finally,a new optimization model based on the REF neural network and genetic algorithm isestablished.With the minimum average contact pressure as objective function,andwith the stress of structure and fatigue life as constraint conditions,design parameters was trained by REF neural network and then was optimized by genetic algorithm.The results indicate that the objective function and the stress of structure decreased after optimization,and fatigue life was improved.