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Research On Adaptively Numerical Control Machining Of Tailing And Leading Edges Of Titanium Hollow Fan Blade

Posted on:2018-06-06Degree:DoctorType:Dissertation
Country:ChinaCandidate:Z C ZhaoFull Text:PDF
GTID:1361330596950592Subject:Mechanical Manufacturing and Automation
Abstract/Summary:PDF Full Text Request
The structural design and aerodynamic behavior of fan blade have great influence on the performance of the aero-turbofan engine.Titanium hollow fan blade(THF blade)is characterised as lightweight,high strength and outstanding aerodynamic efficiency,which is widely used in the current mainstream civil aero-engine and the advanced military aero-engine.THF blade with an accurate sweepback external shape and a complex hollow internal structure is manufactured by a hybrid machining process of superplastic forming/diffusion bonding(SPF/DB)and numeric control(NC)machining.By undergoing multiple-cycle of high pressure and high temperature in the hot forming process,SPF/DB THF blade experiences poor accuracy in external shape,bad stability in hollow structure thickness,uneven distribution in machining allowance and much complexity in stiffness,which make it difficult to manufacture THF blade by using traditional NC machining process.This has became one of the bolltenecks on developing and manufacturing new aero-turbofan engine in our country.This paper aims to develop a high precision NC machining method for the leading and tailing edges of THF blade,which integrates the key technologies of on-machine measurement,shape matching,quick NC programming,machining deformation prediction,error compensation and system integration.The main researches included in this paper are as follow:(1)The structural features and dimensional accuracy of SPF/DB THF blade are analysed.On-machine contact measurement system for complex external shape and on-machine ultrasonic thickness measuremet system for internal hollow structure are built respectively.The calibration methods and the measuring strategies for each measurement system are developed.(2)The methodology of adaptive matching for THF blade CAD model is present.An efficient and accurate registration method under the constraint of the machining allowance distribution between the measured point data and CAD model is developed.Based on the distance between the point-pairs built in the registration process,a stragegy for modifying nominal CAD model of THF blade is studied.The machining allowance distribution of THF blade before and after matching is analysed.(3)A template-based quick NC programming method for machining leading and tailing edges of THF blade is proposed.A global spatial deformation method based on free-form deformation and a local spatial deformation method based on Bezier curve/surface construction are studied respectively.An algorithm for calculating the unit normal vectors of tool positions after deformation is present.(4)The factors that can influence the machining deformation of the leading and tailing edges of THF blade are analysed.A methodology for deformation prediction and error compensation is proposed.A FEM analytical model that combines original residual stress,fixture constraints and cutting force is built.An experiment is carried out to verify the FEM result.Finally,an iterative error compensation strategy for machining complex structural part is given.(5)The integration technology for adaptive machining system is studied.A software for machining leading and tailing edges of THF blade is developed,which is based on UG NX 9.0 and Visual Studio C++.The machining expeiment of titanium hollow structural testing part is carried out by using the developed adaptive machining system.The dimensional accuracy and surface quality of the machined testing part are analysed.The experimental results validate the feasibility of the key technolodiges of adaptive machining and integrated system in this paper.
Keywords/Search Tags:Titanium Fan Blade, Adaptive Machining, On-Machine Measurement, Constraint Registration, Free-Form Deformation, Quick NC Programming, Machining Deformation Prediction
PDF Full Text Request
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