Prediction And Optimization Of The Assembly Accuracy Of Aeroengine Rotors Based On Machine Learning

As one of the key parts of the engine,the aero-engine rotor has an important impact on the actual working state.During the assembly process,due to the uncertainty of the manufacturing errors of the parts and the deformation of the parts after the force is applied,the assembly accuracy of the rotor is difficult to be guaranteed.In addition,the phenomenon of "crankshaft" and "bend-shaped" rotor assembly may also occur.As a result,the one-time success rate of the aero engine rotor assembly is low,and it needs to be assembled repeatedly through manual trial-and-error method and repair method.In this paper,an aircraft engine rotor assembly accuracy prediction and optimization technology based on machine learning is proposed,which combines artificial intelligence algorithm and optimization algorithm to predict the assembly accuracy of aero-engine rotor and improve the assembly efficiency.Aiming at the randomness of manufacturing error of aero-engine rotor parts and the uncontrollability of deformation in assembly process,the finite element and artificial intelligence algorithm are used to achieve efficient and accurate prediction of assembly accuracy.Aiming at the problem of low assembly success rate,an optimization algorithm based on new objective function is proposed,which can avoid the phenomenon of “crankshaft” rotor in the assembly process.The main research of this paper is as follows :(1)Firstly,the research of aviation rotor simulation technology is introduced,including shape extraction and reconstruction technology,simulation pre-processing and post-processing,and the simulation results are analyzed.Finally,an automated method is used to obtain multiple sets of data to build the machine learning data set.(2)Two aircraft rotor accuracy prediction models are constructed: a rigid stack model based on a single equivalent model and an elastic prediction model based on machine learning.First,the rigid model was used to make predictions,and compared with the simulation results,then multiple sets of machine learning models were trained to make predictions,and different machine learning models were compared.(3)Introduced the research on the installation phase optimization algorithm of the aeroengine rotor.Through experiments on a variety of genetic algorithm templates,an optimal algorithm template was selected;and a variety of methods used to eliminate the "crankshaft" rotor were proposed.The objective function is to select the best effect;in addition,the algorithm is cached and optimized,so that the calculation time is extremely fast,only about 10 s,and finally the results are visualized to facilitate observation of the effect.(4)Introduced the design,function and simple use method of intelligent assembly software.In addition,it explained some small details in the software,showing that the software has simple,good operability and user-friendly user experience.