薄壁弧形件装夹布局有限元优化

关于航空结构件加工变形控制的研究是高效数控加工研究的一部分。薄壁弧形零件加工中的弹性变形对加工质量影响很大,而装夹布局影响切削变形的大小和分布。以减少加工中工件最大弹性变形为目标,建立了弧形件铣削加工装夹布局的优化模型,采用商业有限元软件的设计优化模块进行计算。在对计算结果进一步分析的基础上,提出了最终的装夹布局方案,采用该方案可以得到整个加工过程中更低的变形量,变形分布更均匀,为采取相应数控补偿措施提供条件。优化方案和实际加工方案结果基本一致。所提出方法可推广至其他类型工件夹具布局优化设计。     

装夹方案与加工顺序综合优化方法研究

装夹方案和加工顺序对零件加工精度具有重要影响,尤其是对薄壁件的加工.不同的装夹方案和加工顺序都会造成不同的工件变形,甚至使工件报废.因此,基于装夹方案分析的加工顺序优化对薄壁件的加工精度控制有着现实的意义.建立了一个工件-夹具-刀具系统模型,可以分析装夹方案对工件变形的影响,并能以装夹方案为基础分析最优加工顺序.以一个典型薄壁件为例,进行了装夹方案和加工顺序优化分析,得出了装夹方案与加工顺序优化一般性的结论.     

基于BP神经网络的齿圈装夹变形预测研究

针对薄壁齿圈的装夹变形问题,将Abaqus有限元仿真与BP神经网络技术应用到了齿圈装夹变形预测中。根据齿圈实际加工装夹情况,应用Abaqus有限元分析软件,建立了齿圈装夹变形的仿真模型,开展了齿圈装夹变形的有限元分析研究,建立了齿圈装夹力及其径向最大装夹变形之间的关系;以Abaqus有限元仿真数据作为训练样本和检验样本,借助BP神经网络良好的预测精度和非线性泛化能力,通过MATLAB神经网络工具箱,建立了基于BP神经网络的齿圈装夹变形预测数字化模型;并根据检验样本对模型进行了检验,预测值与仿真值之间的相对误差在0.05%之内。研究结果表明:建立的基于BP神经网络的齿圈装夹变形预测数字化模型是准确有效的,可以为智能化大数据加工制造环境下的齿圈装夹参数优化提供准确有效的数据。     

基于神经网络与遗传算法的薄壁件多重装夹布局优化

在多重装夹元件装夹过程中,由于装夹顺序、夹紧力、定位元件位置等装夹布局参数的不同,薄壁件的装夹变形程度也不一样。单个装夹布局参数引起的工件装夹变形规律能够通过有限元方法获得。但是,若同时考虑多个装夹布局参数的影响,仅仅利用有限元方法难以揭示装夹布局参数与装夹变形之间的关系。为此,针对薄壁件的装夹布局方案建立三维有限元模型,以便利用有限元法获取神经网络的训练样本。借助神经网络的非线性映射能力,通过有限的训练样本构建装夹变形的预测模型。以减小工件的最大装夹变形为目标,并根据每一代装夹布局中工件的最大装夹变形定义个体的适应度,建立装夹布局方案的优化模型及其遗传算法求解技术。试验结果表明,网络预测值与相应的有限元仿真值、试验数据之间的相对误差均不超过3%。提出的基于神经网络与遗传算法的装夹变形"分析-预测-控制"方法,不仅能够提高装夹变形的计算效率,而且为薄壁件装夹布局方案的合理设计提供基础理论。     

薄壁件残余应力变形仿真预测与切削参数优化

针对薄壁件铣削残余应力变形难以准确预测的问题,提出了一种仿真预测方法,并在此基础上研究了薄壁件铣削切削参数优化方法。首先,提出了基于工况映射与薄壳应力贴合的残余应力变形仿真预测方法,实验结果表明该方法能够有效预测薄壁件的加工残余应力变形。在此基础上,利用支持向量回归机建立了基于切削参数的残余应力变形响应预测模型;然后,根据所建立的预测模型,采用遗传算法,以残余应力变形为约束、最大加工效率为目标对工艺参数进行优化。结果分析表明,该优化方法获得了最优的加工参数。     

基于RSM的航空叶片切削参数优化

针对航空叶片铣削加工中切削力的预测和控制问题,建立切削力模型,通过正交试验法确定了若干组切削参数方案并完成CAE仿真试验.运用方差分析检验了该预测模型的拟合度和可靠性,研究了切削参数对切削力的影响规律.基于响应曲面法(简称RSM)优化切削参数,获得最佳参数为进给量0.06mm/r,刀具转速2857r/min,铣削深度0...     

钛合金机翼翼根对接缘条铣削变形控制技术研究

钛合金机翼翼根对接缘条类工件结构尺寸大,存在复杂难加工曲面和薄壁型腔,截面形状复杂,装夹困难,加工过程容易变形,切削稳定性差。针对这一问题,借助于有限元分析方法,对钛合金机翼翼根对接缘条的加工变形趋势进行分析仿真,从而选择适宜的工件加工装夹方案。并结合机床-刀具系统动态特性分析,对缘条加工的切削参数进行了确定。通过试验结果表明,该方案可显著提高工件在加工过程中的刚度,减小由于装夹不当引起的工件变形,从而提高加工精度,并对加工所用切削参数进行了验证,提高了切削稳定性,为同类工件加工提供参考。     

基于稳定性理论的机床动态优化方法的研究

基于建立的刀具-工件铣削再生颤振多自由度动力学模型,研究了考虑机床结构参数和加工参数的切削稳定性评价方法。提出了基于稳定性理论以扩大稳定性区域和最大材料切除率为优化目标的机床全生命周期稳定性动态优化方法。在设计阶段,通过对影响稳定性的工艺参数动力修改扩大稳定区域;在生产阶段,进行刀具装夹结构和切削参数的优化,进一步扩大稳定区并确保稳定切削下的最大材料切除率。     

基于B样条的航空薄壁件加工变形预测与控制

针对开发航空薄壁件多点柔性工装系统的需求,研究了航空薄壁件多点柔性数控加工中的变形预测与控制问题。研究了利用有限元分析和Matlab编程建立B样条加工变形数学模型并进行预测的方法及高速铣削切削参数在线优化控制加工变形的原理和方法。研究结果表明:基于B样条曲面数学模型可对加工变形进行预测,切削参数的在线优化能够有效地控制加工变形。研究结果为有效地解决柔性工装的设计和制造提供了必要依据。     

航空结构件数控加工变形及其控制策略

航空结构件加工变形直接影响了结构件的高效、高精加工,这也是航空结构件加工制造所面临的挑战。在对航空结构件分类的基础上分析了航空结构件数控加工变形的机理,指出残余应力、切削力与切削热、工件装夹条件、切削走刀路径等直接影响了数控加工变形。对航空结构件数控加工变形实施控制,具体措施为控制残余应力、优化加工参数、优化走刀路径和优化装夹布局等,为航空结构件数控加工变形控制提供参考。     

虚拟制造中基于刀具变形的复杂曲面加工误差预报

复杂曲面加工过程中刀具的弹性变形是产生曲面加工误差的重要原始误差。着重研究了虚拟制造环境下基于球面铣刀弹性变形的曲面加工误差预报模型。研究并建立了球面铣刀加工复杂曲面的切削力模型和刀具弹性变形模型,在此基础上,分析了曲面生成机理,提出了利用曲面变形敏感系数建立刀具弹性变形对法向加工误差的影响关系。利用该模型可以在实际切削加工前对曲面加工误差进行预报,用以进行误差补偿或切削参数优化。最后,以二维半圆形拉伸曲面为例通过切削实验对本文提出的模型进行了验证。     

基于有限元数值模型和进给速度优化的薄壁件侧铣变形在线控制

为实现在加工过程中对薄壁件侧铣产生的较大切削变形进行在线控制,提出基于有限元数值模型和进给速度优化的在线控制策略。根据薄壁件切削过程的有限元仿真结果,建立数控机床进给速度、切削力、工件切削变形间的数值模型,进而确定用于控制变形的最优目标切削力。在具有开放式模块化的数控系统平台上开发了切削力信号实时采集、滤波功能和基于Brent-Dekker算法的进给速度在线优化策略,并根据滤波后的切削力及相应算法在加工过程中实时调整机床进给速度,保证切削力逐渐接近最优控制目标而实现切削变形的在线控制。试验结果表明,经过进给速度在线优化后的切削过程可将薄壁件侧铣变形控制在规定范围内,同时提高了切削效率。     

基于铣削力精确建模的工件表面让刀误差预测分析

航空航天制造业结构件的高速铣削加工中,在切削力作用下由整体铣削刀具挠度变形所引起的工件表面让刀误差,严重制约零件的加工精度和效率。针对这一问题,通过建立铣削力精确预测模型,结合刀具刚度特点,对工件让刀误差进行预测分析。将切削速度和刀具前角对切削力的影响规律引入二维直角单位切削力预测模型,并通过试验进行相关系数标定。借助等效前角将直角切削力预测系数应用到斜角切削力的预测,通过矢量叠加构建整体刀具三维切削力模型。分析刀具挠度变形对铣削层厚度及铣削接触中心角范围影响规律。基于离散化的刀具模型和切削力模型,建立铣削载荷条件下刀具等效直径悬臂梁模型弯曲变形计算方法。构建以刀具变形对铣削过程影响作用规律为反馈的刚性工件表面让刀误差及切削力柔性预测模型,通过整体铣刀铣削试验验证所建立理论模型的预测精度。     

Study on the response surface model of machining error in internal lathe boring

To achieve high quality and precision of machining products, the machining error must be examined. The machining error, defined as the difference between designed surface and the actual tool, is generally caused by tool deflection and wear, thermal effects and machine tool errors. Among these error sources, tool deflection is usually known as the most significant factor. The tool deflection problem is analyzed using the instantaneous cutting forces on the cutting edge. This study presents a model of the machining error caused by tool deflection in the internal boring process. The machining error prediction model was described by the surface response method using overhang, feed per revolution and depth of cut as the factors for the analysis. The least square method revealed that overhang and depth of cut were significant factors within 90% confidence intervals. Analysis of variance (ANOVA) and residual analysis show that the second-order model is adequate.     

END MILLING SYSTEM COMPLIANCE AND MACHINING ERROR

Tool deflection resulting from cutting forces places a constraint on the achievable precision and productivity in machining. This paper presents an analytical model of machining error, in terms of part form deviation in end milling due to the elastic compliance of cutting tool. Based on the relationship of local cutting forces and chip thickness, the shear loading and bending moment on the tool cross section are presented in terms of cutter angular position. The tool deflection resulting from the bending moment is then established from the principle of virtual work. The resulting deflection of workpiece and machine tool structure is also considered through shear loading analysis. The expression for machining error is derived as a closed-form function of the machining parameters, cutting configuration, material characteristics, and machine receptance. End milling experiments were conducted to verify the analytical model under various cutting conditions. Error maps are presented to illustrate the effects of process conditions on the achievable part accuracy.     

END MILLING SYSTEM COMPLIANCE AND MACHINING ERROR

Abstract

Tool deflection resulting from cutting forces places a constraint on the achievable precision and productivity in machining. This paper presents an analytical model of machining error, in terms of part form deviation in end milling due to the elastic compliance of cutting tool. Based on the relationship of local cutting forces and chip thickness, the shear loading and bending moment on the tool cross section are presented in terms of cutter angular position. The tool deflection resulting from the bending moment is then established from the principle of virtual work. The resulting deflection of workpiece and machine tool structure is also considered through shear loading analysis. The expression for machining error is derived as a closed-form function of the machining parameters, cutting configuration, material characteristics, and machine receptance. End milling experiments were conducted to verify the analytical model under various cutting conditions. Error maps are presented to illustrate the effects of process conditions on the achievable part accuracy.     

Cutting deflection control of the blade based on real-time feedrate scheduling in open modular architecture CNC system

Machining accuracy of thin-walled parts which have low-rigidity is greatly influenced by cutting deflection in flank milling. In this paper, cutting deflection of aero-engine blade during processing is controlled within a required dimensional accuracy based on the strategy of real-time feedrate scheduling which is integrated into an open modular architecture CNC system (OMACS) of five-axis milling machine. The maximum deflection position of blade is determined through combining analytical cutting force model in flank milling and finite element analysis (FEA)-based transient dynamic analysis. Then, the numerical model of blade deflection is established to obtain the numerical relationship among feedrate, cutting force, and blade deflection, which is usually used to get optimized cutting force and feedrate by setting allowable value of blade deflection. To implement blade deflection control during machining, a real-time control strategy of feedrate scheduling based on nonlinear root-finding algorithm of Brent-Dekker and principle of feedrate smooth transition is developed and integrated into OMACS which has functions of real-time cutting force signal processing and real-time feedrate adjustment. Experimental results show that blade deflection is effectively controlled by proposed strategies, machining accuracy, and efficiency are improved.     

Off-line optimization on NC machining based on virtual machining

Virtual machining, based-on the model of a machining system, aims to simulate, evaluate and optimize the actual machining process with high sense of reality. It provides digital off-line optimization tools for NC machining. Taking advantage of virtual machining used in machining process simulation, one can build the framework of optimization system on NC machining so that the processes of reliability verification, cutting parameter optimization and error compensation can be integrated into one system to improve machining processes comprehensively. The optimization is realized via modifying NC programs. Several key issues such as virtual machining, cutting parameters optimization, error prediction and compensation are also highlighted. Optimization systems based on virtual machining have been developed to demonstrate the effectiveness of off-line optimization for different purposes. The results show that the machining process is obviously improved.     

An analytical expression for end milling forces and tool deflection using Fourier series

In this research, a novel and generalized analytical expression of cutting force and tool deflection in end milling is presented as a function of tool rotational angle and other cutting parameters. The discontinuous cutting force function caused by periodic tool entry and exit is changed to an integrable continuous function using Fourier series expansion. Tool deflection is also formulated explicitly by the direct integration of the distributed loads along the helical cutting edges. Cutting conditions, tool geometry, runout components, and the stiffness of tool clamping part are considered in estimating the cutting force and tool deflection. Cumbersome computational procedures needed to check whether segmented cutting edges are engaged in cutting or not are eliminated by this proposed method. The presented analytical approach has advantages in flexibility, prediction time, and accuracy as compared with other numerical techniques. In addition, the effects of cutting conditions and run-outs, such as eccentricity and tilting on the cutting force and tool deflection, can be analyzed quantitatively in the time domain or frequency domain. The validity and effectiveness of the suggested method are verified through a series of cutting tests. The model presented in this research can be used in real-time machining error estimation and cutting condition selection for error minimization since the form accuracy is easily estimated from the acquired tool deflection curve.     

Five-axis tool path and feed rate optimization based on the cutting force–area quotient potential field

Feed rate assignment in five-axis surface machining is constrained by many factors, among which a particularly critical one is the deflection cutting force on the tool: while a larger feed rate increases the machining productivity by shortening the total machining time, it nevertheless inevitably enlarges the deflection cutting force as well, which will cause the tool to be more prone to bending and the machine more prone to vibration, thus adversely degrading the surface finish quality. In this paper, we present a new five-axis tool path generation algorithm that strives to globally maximize feed rate for an arbitrary free-form surface while respecting a given deflection cutting force threshold. The crux of the algorithm is a new concept of the (cutting) force–area quotient function—at any cutter contact point on the surface, the maximal effective material removal rate (with respect to the deflection cutting force threshold) is a continuous function of the feed direction. This function induces a potential field on the surface and based on which an efficient tool path generation algorithm is designed. Preliminary experiments show that substantial reduction in total machining time can often be achieved by the proposed algorithm.