基于敏感度分析的机床关键几何误差元素辨识与评定

为了正确识别和判定机床关键几何误差元素对机床精度设计的影响,以PCV-620立式加工中心为研究对象,采用多体系统理论建立机床空间误差模型,从而得到机床几何误差元素与机床精度之间的关联函数。对空间误差模型进行灵敏度分析,获得机床各运动方向的局部灵敏度系数,完成机床关键几何误差元素的初步辨识。以局部灵敏度系数为基础,提出一种与局部灵敏度系数和工作空间中任意位置处的几何误差元素值相关的全局灵敏度系数计算方法,将其作为机床关键几何误差元素的辨识和评定标准,分析得到PCV-620立式加工中心的关键几何误差元素包含3项定位误差、3项垂直度误差和5项直线度误差。     

车铣复合数控机床空间误差建模和补偿

空间误差是影响车铣复合数控机床零件加工精度的最重要因素，现有方法对机床各轴的定位精度提升效果不好，为此设计车铣复合数控机床空间误差建模和补偿方法。忽略机床两个旋转轴的位置无关误差，通过齐次坐标变换理论构建其几何误差辨识模型，对几何误差辨识模型进行简化，实现两轴的几何误差辨识。在工件坐标系下，根据旋转轴几何误差辨识结果，采用多体理论构建机床空间误差模型。基于此误差模型，利用理想状态的逆运动学设计同步空间误差补偿策略，通过迭代方式对各轴补偿值进行计算，实现空间误差补偿。测试结果表明：设计方法补偿后，实验机床X轴、Y轴、Z轴的定位精度提升了0.6μm,B轴、C轴的定位精度提升了4″、3″,各轴的重复定位精度有很大提升，机床的反行程实验圆度也有所提升。     

立式加工中心关键几何误差判定与量化验证

针对现阶段机床空间误差模型不完整且传统灵敏度分析存在局限性,导致其关键几何误差溯源不准确,以及关键几何误差判定结果难以量化验证的问题,以某立式加工中心为研究对象,提出一种机床关键几何误差判定与量化验证方法。以旋量理论为基础,研究某立式加工中心空间误差建模,以输出机床完整空间误差模型;在此基础上,以基于传统局部灵敏度分析为基础,利用误差贡献度因子判定机床关键几何误差;借助数值模拟实验对判定结果进行量化验证。结果表明：相较于传统灵敏度分析结果,利用误差贡献度因子判定关键几何误差的结果更准确;基于误差贡献度因子的判定结果,不仅能量化几何误差相对机床空间误差的影响程度,同时可为机床部件制造精度设计提供理论参考。     

精密卧式加工中心空间误差的建模、测量与补偿技术

采用空间误差补偿技术,可有效提高数控机床的空间定位精度。以一台精密卧式加工中心为对象,系统阐述其几何误差补偿中的关键问题及解决方案。通过三维误差建模与分析,得到该机床的21项几何误差中有17项需要测量和补偿,另外4项误差对机床定位精度的影响甚微。以此为依据,设计误差测量及补偿方案,并给出误差的具体测量方法和补偿结果。结果表明:经过一次系统地误差测量与补偿,精密卧式加工中心的空间定位精度可以提高50%～70%;合理规划和实施空间误差测量,可大幅提高测量效率。     

立式加工中心空间误差验证及补偿

为提高某立式加工中心整机加工精度,借助旋量理论建立完备立式加工中心空间误差模型,在此基础上实现机床空间误差有效补偿.以旋量理论为基础推导并建立机床刀具运动链与工件运动链运动学正解,分析机床21项几何误差原理,在考虑21项几何误差的基础上建立该立式加工中心完备空间误差模型;利用九线法完成各项几何误差辨识;基于旋量运动学正解求解机床运动学逆解后得出运动轴实际运动路径,并通过体对角线实验对比补偿前后的效果.结果表明:所提补偿方法补偿效果显著,验证了机床空间误差模型的准确性,实现了提高机床加工精度的目的.     

基于激光跟踪仪的数控机床空间误差测量及补偿

为了改变机床空间误差综合性的测量手段和补偿技术在国内机床制造和生产中应用较少的现状和研究数控机床空间精度提升方法，介绍数控机床平动轴的21项误差和激光跟踪仪的空间误差测试原理，阐述测量与辨识机床空间误差的步骤和方法。在桥式五轴加工中心上进行空间误差测试，给出数控机床空间误差结果，并生成误差补偿文件，通过西门子的VCS功能进行了误差补偿。并对比分析了补偿前后的21项误差，对补偿前后数据的差异进行原因分析，并通过对机床空间体对角线的测量验证了空间误差测量与补偿的实际效果，补偿后误差缩小为原来的11.2%,应用该技术能够大大提高机床的空间精度。     

基于空间样条曲线的数控五轴机床误差辨识研究

在划分球杆仪空间误差辨识区域的基础上,基于NUBRS空间样条曲线生成辨识轨迹。分析球杆仪空间辨识原理,并依据此原理建立适用于NUBRS空间曲线的误差辨识模型,该模型包含18个误差因素。依据五轴机床多体运动学的分析结果,提出了一种空间误差辨识方法。通过激光干涉仪的测量及误差补偿,在消除9项空间位移误差的基础上,使用生成的NURBS曲线对剩余9项角度误差因素进行辨识。经实验验证,在进行NUBRS曲线修正后,该方法可有效辨识出3个坐标系中的18项误差因素。     

基于热传递函数的机床主轴热关键点辨识方法

针对机床热误差补偿技术中热态特性建模与热关键点辨识困难问题,提出一套较完善的主轴热态特性建模方法与热关键点快速辨识技术。考虑主轴系统温度与热变形等因素,建立主轴热态特性分析模型,结果表明:模型预测值与某精密卧式加工中心的热误差实测量值之间的误差均在20%以内,说明了所提建模方法的正确性。将模型输出结果用于主轴热关键点辨识,根据12个测点的热传递函数值筛选出6个热关键点;利用6个关键点的数据,基于BP神经网络建立一种主轴热误差预测模型;对比BP神经网络预测的输出值与热态特性模型的输出值,结果表明:最大误差为-0.060 36μm、最大相对误差为-0.200 6%,验证了所提热关键点辨识方法的有效性。     

基于可补偿误差项的机床几何误差建模与补偿技术

提出一种可分离数控机床刀具与工件之间可补偿与不可补偿几何误差源的方法;依据可补偿误差项建立误差补偿模型;采用激光干涉仪+直角镜的垂直度直接辨识方法检测出几何误差,导出补偿量计算式,选用西门子840DNC的几何误差补偿模块进行补偿实验,其中8项指标补偿后的几何误差减小比例在16.5%～92%,补偿效果显著。实践证明本文作者所提出的机床可补偿几何误差的建模与补偿方法是有效和可行的,其补偿方法同样可以用于不同类型的机床。     

内螺纹磨床的几何误差建模及灵敏度分析

为提高内螺纹磨床的加工精度，建立了内螺纹磨床的几何误差模型并对其进行全局灵敏度分析。首先，基于多体系统理论和齐次坐标变换的方法，建立了砂轮磨削加工系统和砂轮修整系统的几何误差模型，通过误差传递推导出整机加工精度模型；其次，考虑到几何误差作用的随机性和耦合性，建立基于Sobol的拟蒙特卡洛法的全局灵敏度分析模型，识别出影响磨床加工精度的关键几何误差因素。利用MATLAB编写软件，以SCS-180TB的内螺纹磨床的加工误差模型进行灵敏度计算，并验证模拟次数的收敛性。     

Development of a multi-step measuring method for motion accuracy of NC machine tools based on cross grid encoder

Machining accuracy is directly influenced by the quasi-static errors of a machine tool. Since machine errors have a direct effect on both the surface finish and geometric shape of the finished work piece, it is imperative to measure the machine errors and to compensate for them. A revised geometric synthetic error modeling, measurement and identification method of 3-axis machine tool by using a cross grid encoder is proposed in this paper. Firstly a revised synthetic error model of 21 geometric error components of the 3-axis NC machine tools is developed. Also the mapping relationship between the error component and radial motion error of round work piece manufactured on the NC machine tools are deduced. Aiming to overcome the solution singularity shortcoming of traditional error component identification method, a new multi-step identification method of error component by using the cross grid encoder measurement technology is proposed based on the kinematic error model of NC machine tool. Finally the experimental validation of the above modeling and identification method is carried out in the 3-axis CNC vertical machining center Cincinnati 750 Arrow. The entire 21 error components have been successfully measured by the above method. The whole measuring time of 21 error components is cut down to 1–2 h because of easy installation, adjustment, operation and the characteristics of non-contact measurement. It usually takes days of machine down time and needs an experienced operator when using other measuring methods. Result shows that the modeling and the multi-step identification methods are very suitable for ‘on machine’ measurement.     

Identification and compensation of geometric errors of rotary axes on five-axis machine by on-machine measurement

The geometric errors of rotary axes are the fundamental errors of a five-axis machine tool. They directly affect the machining accuracy, and require periodical measurement, identification and compensation. In this paper, a precise calibration and compensation method for the geometric errors of rotary axes on a five-axis machine tool is proposed. The automated measurement is realized by using an on-the-machine touch-trigger technology and an artifact. A calibration algorithm is proposed to calibrate geometric errors of rotary axes based on the relative displacement of the measured reference point. The geometric errors are individually separated and the coupling effect of the geometric errors of two rotary axes can be avoided. The geometry error of the artifact as well as its setup error has little influence on geometric error calibration results. Then a geometric error compensation algorithm is developed by modifying the numeric control (NC) source file. All the geometric errors of the rotary errors are compensated to improve the machining accuracy. The algorithm can be conveniently integrated into the post process. At last, an experiment on a five-axis machine tool with table A-axis and head B-axis structure validates the feasibility of the proposed method.     

非球面超精密磨削误差建模与补偿研究

为提高课题组自研的超精密磨床加工精度,基于多体系统理论,运用齐次坐标变换原理,分析该超精密磨床37项几何误差来源,对非球面超精密磨削的综合误差建模。超精密磨床的多项几何误差元素已在制造阶段标定、补偿,取砂轮对刀误差和砂轮轮廓半径磨损误差作为主要面形误差来源,分别推导其对综合误差的传递函数,分析误差辨识方法,建立误差修正补偿模型,提出基于直接补偿的点补修正法。试验结果表明：建立的综合误差模型正确,根据误差辨识方法和修正补偿模型,修正误差后面形误差显著降低,有效提高面形精度。     

五轴联动立式磨床几何误差灵敏度研究

以一台五轴联动立式磨床为例,提出了一种分析几何误差灵敏度的方法。基于多体系统理论和齐次坐标变换建立了五轴联动立式磨床的33项几何误差模型,利用求导的方式给出了几何误差灵敏度分析的数学模型。并根据该模型找出了关键几何误差,研究了关键几何误差的灵敏度系数随平移轴运动的变化规律,研究结果为精密机床改进设计和误差的实时补偿提供了理论依据。     

Adaptive Monte Carlo applied to uncertainty estimation in five axis machine tool link errors identification with thermal disturbance

Knowledge of a machine tool axis to axis geometric location errors allows compensation and corrective actions to be taken to enhance its volumetric accuracy. Several procedures exist, involving either lengthy individual test for each geometric error or faster single tests to identify all errors at once.This study focuses on the closed kinematic chain method which uses a single setup test to identify the eight link errors of a five axis machine tool. The identification is based on volumetric error measurements for different poses with a non-contact Cartesian measuring instrument called CapBall, developed in house.In order to evaluate the uncertainty on each identified error, a multi-output Monte Carlo approach is implemented. Uncertainty sources in the measurement and identification chain – such as sensors output, machine drift and frame transformation uncertainties – can be included in the model and propagated to the identified errors. The estimated uncertainties are finally compared to experimental results to assess the method. It also reveals that the effect of the drift, a disturbance, must be simulated as a function of time in the Monte Carlo approach.Results shows that the machine drift is an important uncertainty source for the machine tested.     

基于多体系统理论的车铣中心空间误差模型分析

数控机床的误差建模是进行机床运动设计、精度分析和误差补偿的关键技术,也是保证机床加工精度的重要环节.本文利用多体系统理论来构建超精密数控机床的几何误差模型,该模型简便、明确,不受机床结构和运动复杂程度的限制,为计算机床误差、实现误差补偿和修正控制指令提供了理论依据.在机床实际应用中,可以利用由精密机床误差建模所推导出的几何位置误差来修正理想加工指令,控制机床的实际运动,从而实现几何误差补偿,提高机床加工精度.