Research On Parameter Optimistic Method And Experiment Of Cutting Tool For Processing Woodblock In Paving Woodiness Ground

The traditional design of wood cutting tools is making of material,molding of cutting tools,cutting experiment,optimazing of structure and gemetry parameter and final product of cutting tools.This always takes us a lot of time and money and it also needs lots of manpower. After finite-element method was brought out,research efficiency and precision have been improved.However,the early finite-element method often processes mathematical modeling first and then programming computation when researching cutting tools,which not only wasted time but also being difficult in precision control.In order to solve above problem,numerical simulation techniques is introduced to the research of wood cutting process.Through analogue simulation to analyze changing rule of wood mechanics behavioural and verificate intensity of cutting tool when cutting,its geometric parameter is determined.This paper also applies radial basis function neural network based on blur self adapting Kallman filter to optimize design of wood cutting tools,which is a big innovation and makes development cutting tool in high precision possible.General clue of this paper is simulating wood stress field change in initial cutting stage and working out weighted boundary condition of cutting tools first,and then based on which, finding out shear stress and maximal effective stress when wood cut-ted.Meanwhile,finite element comparative analysis is proceeded on distortion and stress field by changing tool orthogonal rake.Finally the cutting tool geometric parameter can be determined.For woodiness material,different properties need different geometric parameter of cutting tools.Processing velocity,working quality and service life will be improved greatly if material properties is correspondent with parameter of cutting tool.In this paper,virtual optimum design for cutting tool is proceeded by ANSYS,MATLAB and radial basis function neural network based on blur self adapting Kallman filter,which can supply a sort of deservable referenced means.Main research in this paper is as follows:(1)Twelve elastic constants,shearing-strength of wood along the grain and tensile strength parallel to grain are measured by test.(2) The preprocessor of ANSYS is applied to set up fmite element model for wood cutting based on theoretical anylysis and by applying load,numerical simulation at initial stage when wood cutting is accomplished.Substituting solid45 with solid95 and applying numerical simulation to the research on cutting is a innovation point.(3) By applying postprocessor of ANSYS to abstract result and compare it with test result. it is shown clearly that the method presented in this paver is feasible in analysis of w(?)d cutting procedure.(4)Due to the particularity of woodiness material,tree species,elastic constant,strength of extension,water ratio,cutting direction etc.should be considered,which makes the selection of geometric parameter of wood cutting tools possess strong pertinence.In this paper,optimized geometric parameter corresponding to different wood is determined by simulating cutting procedure and verificating intensity of cutting tool based on ANSYS.(5) Optimum design program for cutting tools is set up by radial basis function neural network based on blur self adapting Kallman filter,which could develop cutting tools with high efficiency and high precision.Applying radial basis function neural network based on blur self adapting Kallman filter to optimum design of wood cutting tools is another big innovation point.