Surface Quality Analysis And Machining Process Parameters Optimization Of Thin-Walled Workpiece In Milling

Thin-walled workpieces are substantively used in aeronautic and aerospace industries.Due to the characteristic of large size and weak rigidity,deflections of the workpiece will be easily induced during the cutting process.As a result,the machining accuracy and the surface quality of the workpiece are extremely violated,and in the worst case useless products will be produced.Therefore,investigations on the stability and dynamical errors of the cutter/workpiece coupling system and the optimization of machining process parameters are of great significance to develop error control strategy and to ensure the machining quality.This major research works are included.The thesis uses the FLN(Floquet-Nyquist method)and convolution milling force models to predict the milling of stability.This theory can be used to predict when milling spindle speed of chatter and critical cutting depth.The polynomial network is adopted to construct a prediction model for surface roughness.The grey-relational analysis combined with two-stage experimental design,back-propagation neural network(BPNN),Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)is proposed to achieve an optimal design of cutting parameters.The main research contents are as follows:(1)First,milling force model carried out a full analysis.On the basis of theoretical analysis and convolution models milling force required and their corresponding Fourier expansion carried out a full analysis.Lay the theoretical foundation for the subsequent surface quality and stability analysis.(2)Second,a stability prediction model is built.To avoid the occurrence of chatter,it was necessary to fully understand the causes of chatter and to establish chatter stability lobes for prediction of chatter stability.While this article uses the FLN and convolution milling force models to predict the milling of stability.This theory can be used to predict when milling spindle speed of chatter and critical cutting depth and,through this theoretical model to analyze different cutting mode(up milling,down milling),the cutting conditions(radial cutting depth,cutting constants),the tool geometry(cutter flute,helix angle)on stability in milling system..(3)Third,establish a surface topography and surface position error can simultaneously model.The machined surface topography,surface roughness and surface location error are very important evaluation indexes for machining quality,which directly affect the performance characteristics of the machined workpiece.In this thesis,a new model of the arbitrary point of the cutter edge with respect to the arbitrary frame of the machining feature point is built.The proposed model incorporates the surface location error into the relative motion of the cutter with respect to the workpiece using the harmonic balance methods.Further,a nonlinear programming problem is proposed to obtain the scallop value of an arbitrary point on the nominal machined surface.Since the obtained scallop value of each point can be used for constructing the topography of the machined surface as well as calculating the surface roughness,the proposed model can simultaneously predict the surface topography,surface roughness and surface location error.Simulations and experiments indicate that feed per tooth,runout and number of cutter teeth are vital factors influencing the topology of the machined surface.The surface location error of the cutter due to the number of cutter teeth is an essential factor that influences the parallelism or perpendicularity of the machining feature.The effectiveness and feasibility of the proposed method is verified by a machining example.(4)Fourth,optimize the thin-walled milling process parameters.This study proposes optimization models of process parameters in milling process in order to solve those problems.On the basis of the previous section,the process parameters were optimized.And make a further analysis of the surface topography and stability.The model is an optimization based on BPNN using hybrid algorithms.The data gained from the Taguchi experiment are applied to train in BPNN so as to generate an S/N ratio predictor and quality predictor.The first stage is to optimize the S/N ratio.The S/N ratio predictor then works together with GA for universal search to acquire the process parameters and to maximize the S/N ratio of the quality responses.This stage intends to minimize the variance in process.As for the second stage,process optimization of the objective quality is investigated.Hybrid GA-PSO is associated with the S/N ratio predictor and the quality predictor for universal search.The process parameters gained from the first stage is set for the initial values.The Hybrid GA-PSO is then used to find the optimal parameter settings that tally best with the quality standard and make it most stable in milling process.Comparisons of simulated and measured results show that the proposed models and methods are reliable and efficient.In sunmmary,focusing on how to predict the machined surfaced quality and optimizes the milling process parameters.In this paper,a new model of the arbitrary point of the cutter edge with respect to the arbitrary frame of the machining feature point is built.The proposed model can simultaneously predict the surface topography,surface roughness and surface location error.The confirmation results show that two-stage optimization system turns out with the best performance.The approach not only enhances the stability in the whole the milling process of thin-walled workpiece including the quality in workpiece surface but also saves the cost and time spent from the mold design to manufacturing.Consequently,the manufacturing cycle can be shortened and traditional experience-based process planning can be greatly improved for a combination with the theoretical analysis,mathematical modeling and numerical simulation to achieve the optimal design.