【作者】 郭秀荣；

【导师】 陆怀民；

【作者基本信息】 东北林业大学 ， 机械设计及理论， 2008， 博士

【摘要】 在传统的刀具设计中,一般先进行材料研制,再制成刀具,而后切削试验,反复修改数次,才能推出一个刀具品种。这不仅要耗费大量时间和财力,而且效率往往很低,预见性不强。有限元法产生后,使得刀具研究的效率和精度有所提高,但早期的有限元法研究刀具多是先数学建模再编程计算,这种思路不仅费时而且精度不易控制。为了解决上述问题,本文将数值模拟技术引入到木材切削的研究中来,通过仿真模拟,分析切削时木材力学性能的变化规律及对刀具强度进行校核,确定刀具几何参数。本文还将基于模糊自适应卡尔曼滤波的径向基函数神经网络应用到木工刀具的优化设计中,这是本文的一大创新点,使高效、高精度地研制刀具成为可能。本课题的总体思路是通过大型有限元软件ANSYS来模拟切削初始阶段中木材的应力场变化,进而得出刀具受力的边界条件,依此为基础求出木材受切时的剪应力及最大等效应力,同时通过变化刀具前角分别对受切工件的变形及应力场进行有限元比较分析,从而确定刀具的优化前角;优化前角确定后,再通过对刀具进行强度校核确定后角优化值。木质材料材性不同,所需刀具几何参数也不同。对于不同的木质材料,如果能使用与之相适应的不同参数的刀具则会大大提高木质材料加工速度、加工质量及刀具的使用寿命。本文应用ANSYS、MATLAB及基于模糊自适应卡尔曼滤波的径向基函数神经网络对刀具进行虚拟优化设计,可为刀具优化设计提供一种值得参考的方法。本文所开展的主要工作如下:(1)通过试验测定不同木材的12个弹性常数及顺纹抗剪强度和顺纹抗拉强度。(2)在理论分析的基础上,应用ANSYS的前处理器建立木材切削的有限元模型,通过施加载荷,计算分析,完成对木材切削初始阶段的数值模拟。用SOLID95单元代替SOLID45单元,把数值模拟方法运用于木材切削的研究中是本文的一大创新点。(3)应用ANSYS后处理器提取计算结果并与试验结果进行比较,说明用本文所提出的方法进行木材切削过程分析是可行的。(4)由于木质材料性质的特殊性,切削必须要考虑树种、弹性常数、抗拉强度、含水率、切削方向等因素,这也使得木工刀具几何参数的选择具有较强的针对性。本文在ANSYS分析的基础上,通过对木材切削过程进行模拟、对刀具强度进行校核确定加工不同木材时所对应刀具的优化几何参数。(5)在MATLAB环境下,应用基于模糊自适应卡尔曼滤波的径向基函数神经网络编制刀具优化设计程序,应用该程序可高效、高精度地设计刀具。将基于模糊自适应卡尔曼滤波的径向基函数神经网络应用到木工刀具优化设计中是本文的又一大创新点。更多还原

【Abstract】 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.更多还原