基于球杆仪的直线轴位置相关误差辨识研究

针对多轴机床空间误差检测及辨识方法成本高、时间长等问题,提出一种新的基于球杆仪测试的直线轴位置相关几何误差辨识方法。分别建立各平面内轴运动误差模型,并采用多项式对误差元素预拟合,以常规的三平面圆弧轨迹测量获取误差数据,并基于最小二乘法求解拟合系数,替代直接对误差元素具体数值求解的传统方法,实现对各直线轴位置相关误差元素的辨识。通过实验验证了辨识结果的正确性和有效性,该方法对机床直线轴误差辨识、补偿具有参考价值。     

球杆仪安装误差对测量误差的影响规律及其分离方法

通过齐次坐标变换理论建立了五轴数控机床摆动轴几何误差的辨识模型,深入分析了在误差测量过程中球杆仪磁性球座的安装误差对测量误差的影响规律,提出了一种球杆仪磁性球座安装误差的分离方法,并在五轴数控机床上进行了实验验证。结果表明,该方法可以有效分离出球杆仪磁性球座的安装误差,提高摆动轴几何误差的测量和辨识精度。     

基于球杆仪的数控车床几何误差检测和补偿

采用Renishaw的QC10球杆仪可以方便快捷地测量出数控车床的圆度误差,同时用软件对测量数据进行处理,从中分离出反向越冲、反向间隙、直线度、伺服不匹配和比例不匹配等误差项,并可以分析出各种误差项所占的百分比。在精确掌握数控车床球杆仪误差诊断表的基础上,采用电气参数优化实现误差补偿,补偿实验结果证明该误差识别与补偿方法省时有效。     

基于球杆仪数控机床误差补偿方法研究

分析了基于球杆仪的数控机床误差补偿测试原理 ,讨论了由于角度偏差引起的误差 ,指出基于球杆仪误差补偿模型的缺陷 ,提出采用该种模型必须精确测量角度值     

直线轴热定位误差解耦与分步建模研究

针对直线轴热定位误差同时与位置、温度相关,传统建模方法工作量大、效率低且变工况下预测精度较差等问题,本文提出一种直线轴热定位误差解耦与分步建模方法。首先,基于最小二乘线性拟合对多工况下测量的热定位误差解耦,获得仅与温度相关的斜率参数与截距参数;其次,分别使用绝对温度和相对温度作为输入变量对斜率参数和截距参数回归建模,得到二者与温度的映射关系,结合斜率与截距,建立热定位误差模型;最后,基于建立的模型对全新工况下的热定位误差进行了预测,可实现最大残差1.6μm,相比直接建模方法预测精度显著提升,表明了模型的有效性。     

基于球杆仪的五轴数控机床误差快速检测方法

几何误差是五轴数控机床重要误差源,针对传统测量方法仪器昂贵、测量周期长问题,提出基于球杆仪的五轴数控机床几何误差快速检测方法。对于机床的平动轴误差,利用多体系统理论及齐次坐标变换法,建立平动轴空间误差模型,通过球杆仪在同一平面不同位置进行两次圆轨迹,辨识出4项平动轴关键线性误差;针对五轴机床的转台和摆动轴,设计基于球杆仪的多条空间测试轨迹,完整求解出旋转轴12项几何误差。实验结果显示,所提方法获得转角定位误差与激光干涉仪法最大误差为0.001 8°,利用检测结果进行机床空间误差补偿,测试轨迹偏差由16μm降至4μm,为补偿前的25%,验证了方法的有效性。提出的五轴机床几何误差检测方法方便、便捷,适用于工业现场。     

球杆仪用于数控机床几何误差补偿的研究

介绍了利用球杆仪测试和辨识数控机床几何精度的方法,在精确掌握三坐标数控机床几何误差的基础上,通过建立三轴数控机床的几何误差模型,利用误差综合补偿软件进行了补偿实验.结果表明,机床的各项误差都有所降低.     

立用球杆仪分析数控机床误差

数控机床是一个典型的多体系统，若干部件以不同形式连接在一起，可以有多个分支。对数控机床误差源的大量研究表明，机床几何误差、热变形误差和载荷变形误差占总误差中的比例大，而刀具、夹具和工件误差也可列入总误差之中。对于以上的误差处理有两种方法：其一是误差避免；其二是误差补偿。     

基于球杆仪的数控机床误差识别与补偿

数控机床设备的运行技术误差,对于我国数控机床设备的生产技术水平具有深刻影响,本文围绕基于球杆仪的数控机床误差识别与补偿选取两个方面展开了简要的论述分析。     

四轴数控机床转台几何误差检测与分离

针对某公司研制的THM46100高精度四轴卧式加工中心,提出了一种快速分离转台六项几何误差的方法。借助球杆仪分别采用平行于X、Y、Z轴及锥形的特殊安装方式,进行了转台几何误差分离实验,基于转台几何误差辨识模型,辨识出转台运动产生的3项位移误差δx、δy、δz和2项转角误差εx、εz,并配合激光干涉仪与回转分度仪对转台的定位误差εy进行了实验检测,从而实现了转台6项误差的识别。该方法与传统单项测量法相比,具有操作简单,检测效率高,适合现场测量等优点。     

Using a double ball bar to identify position-independent geometric errors on the rotary axes of five-axis machine tools

A measuring method using a double ball bar (DBB) is proposed for identifying the eight position-independent geometric errors (PIGE) on the rotary axes of five-axis machine tools. Three measuring patterns are used, in which the translational axes are kept stationary and only two rotary axes move to obtain a circular trajectory. In this way, the effects of translational axes are totally excluded, and the measurement accuracy is improved. Motion equations, describing how the A-axis and C-axis move simultaneously to realize a circular trajectory, are presented. The influence of each deviation on the measurement patterns is simulated, and analytical solutions for the eight PIGEs are demonstrated. Finally, the measuring method is verified in a five-axis CNC machine tool. Experimental results confirm that the method provides precision results for the eight PIGEs.     

Position-dependent geometric errors measurement and identification for rotary axis of multi-axis machine tools based on optimization method using double ball bar

Geometric errors measurement and identification for rotary table are important. However, precisely adjustment for the setup position of a double ball bar in each measurement pattern is inconvenient. This study proposes a novel optimization identification method using a double ball bar to recognize the position-dependent geometric errors (PDGEs) of rotary axis. A mathematical model for ball bar measurement is firstly constructed to map the relationship between measurement direction and position of the double ball bar. And then, the setup positions of the double ball bar for PDGEs identification are analyzed. According to analysis for setup positions of the double ball bar, simplified measurement patterns would be conducted by adjusting only two setup positions for the ball bar and thus reduce the procedure of accurate adjustment for the ball bar. The PDGEs can be fitted as an nth B-spline curve, on the account of its being smooth and continuous. To identify the PDGEs, an optimization method, by computing the suitable value of control points of nth B-spline curve of errors to minimize the optimal value of the target function between the actual measured value and the value derived from a theoretical measurement model, is proposed. Moreover, in order to obtain the accurate value of control points of the error curve, the sensitivity analysis is conducted to acquire the sensitivity matrix with respect to control points of errors. The PDGEs are able to be identified simultaneously after calculating the appropriate values of control points of errors. The proposed identification method is validated by simulation and experiment. The results prove the effectiveness of the proposed method.     

Geometric characterisation and simulation of position independent geometric errors of five-axis machine tools using a double ball bar

A double ball bar (DBB) is extensively used to evaluate the geometric and dynamic performance of three-axis machine tools by means of the XY, YZ and XZ planar circular tests. Errors can be estimated by comparing them with known error profiles. However, such geometric interpretation of error plots of five-axis machine tools is limited. In this paper, a five-axis machine tool model is established with Homogeneous Transformation Matrices (HTMs), laying the foundation for characterising particular geometric shapes induced by various Position Independent Geometric Errors (PIGEs) of all five axes. A testing scheme is proposed to evaluate the target five-axis machine tool in two major steps: testing the rotary axes individually and testing the linear-rotary axes couples. In the first step, each rotary axis is tested with two substeps, with and without the extension bar on the DBB. The second step requires each linear and rotary axes combination to move simultaneously. Both approaches are performed with only one setup, thus simplifying the setup procedure and reduce the machine down time. To show the validity of the method, PIGEs for each axis are simulated with the given machine tool model. Several DBB trajectories are simulated using the machine tool model. Compared with the actual testing plots, the simulated DBB error plots are helpful to diagnose the PIGEs of linear and rotary axes based on their particular geometric shapes. The results suggest that the proposed method along with the given error characteristics can be used as a fast indication of a five-axis machine tool’s performance.     

Plane kinematic calibration method for industrial robot based on dynamic measurement of double ball bar

A new calibration method is proposed to improve the circular plane kinematic accuracy of industrial robot by using dynamic measurement of double ball bar (DBB). The kinematic model of robot is established by the MDH (Modified Denavit-Hartenberg) method. The error mapping relationship between the motion error of end-effector and the kinematic parameter error of each axis is calculated through the Jacobian iterative method. In order to identify the validity of the MDH parameter errors, distance errors and angle errors of each joint axis were simulated by three orders of magnitude respectively. After multiple iterations, the average value of kinematic error modulus of end-effector was reduced to nanometer range. Experiments were conducted on an industrial robot (EPSON C4 A901) in the working space of 180 mm × 490 mm. Due to the measuring radius of DBB, the working space was divided into 30 sub-planes to measure the roundness error before and after compensation. The average roundness error calibrated by the proposed method at multi-planes decreased about 21.4%, from 0.4637 mm to 0.3644 mm, while the standard deviation of roundness error was reduced from 0.0720 mm to 0.0656 mm. In addition, by comparing the results of positioning error measured by the laser interferometer before and after calibration, the range values of motion errors of end-effector were decreasing by 0.1033 mm and 0.0730 mm on the X and Y axes, respectively.     

Three-point method for measuring the geometric error components of linear and rotary axes based on sequential multilateration

The linear and rotary axes are fundamental parts of multi-axis machine tools. The geometric error components of the axes must be measured for motion error compensation to improve the accuracy of the machine tools. In this paper, a simple method named the three-point method is proposed to measure the geometric error of the linear and rotary axes of the machine tools using a laser tracker. A sequential multilateration method, where uncertainty is verified through simulation, is applied to measure the 3D coordinates. Three non-collinear points fixed on the stage of each axis are selected. The coordinates of these points are simultaneously measured using a laser tracker to obtain their volumetric errors by comparing these coordinates with ideal values. Numerous equations can be established using the geometric error models of each axis. The geometric error components can be obtained by solving these equations. The validity of the proposed method is verified through a series of experiments. The results indicate that the proposed method can measure the geometric error of the axes to compensate for the errors in multi-axis machine tools.     

Parametric modeling and estimation of geometric errors for a rotary axis using double ball-bar

In this study, the geometric errors of the rotary axis of machine tools are modeled parametrically and estimated using a double ball-bar. To estimate the geometric errors from the measured data, they are defined as position-dependent/position-independent geometric errors. The position-dependent and position-independent geometric errors are modeled as nth-order polynomials with C ¹-continuity and constants, respectively. Additionally, the set-up errors which are inevitable during the installation of the double ball-bar are modeled as constants to increase the accuracy of the estimation process. The measurement paths are designed to increase the sensitivity of the geometric errors in the measured data. The position of the balls constituting the double ball-bar is calculated in the reference coordinate system using the homogeneous transform matrices. The ball-bar equation is applied to determine the relation between the measured data and geometric errors. The linearized relations between them are derived by eliminating the higher-order error terms. The parameters of the modeled geometric errors and set-up errors are calculated using the least squares method. Finally, the geometric errors are estimated using the calculated parameters. The validity of the proposed method is tested through simulations and it is used to estimate the geometric errors of the rotary axis of five-axis machine tools.     

The optimal design of a measurement system to measure the geometric errors of linear axes

In this paper, a measurement system consisting of an L-type reference mirror and five capacitive sensors is analyzed and optimized to measure the geometric errors of linear axes more accurately. The positions of the reference coordinate system and capacitive sensors are optimized to minimize the standard uncertainty of estimated geometric errors, which is due to the standard uncertainty of the component—the L-type reference mirror and the capacitive sensors. Primarily, the flatness of the L-type reference mirror and the linearity of the capacitive sensors cause the component uncertainties. The capacitive sensors fixed on the linear axis are moved, and the L-type reference mirror is fixed on the base of the machine tool to eliminate Abbe's error, which is proportional to the command position of a linear axis. Five geometric errors of a linear axis are measured with a single setup and single measurement, simply. Finally, the optimized measurement system is applied to measure the geometric errors of linear axes X and Y of a three-axis machine tool. And the standard uncertainties of the measured geometric errors are calculated based on the specifications of the L-type reference mirror and the capacitive sensors.     

五轴机床旋转轴误差的在机测量与模糊径向基神经网络建模

考虑五轴机床中的旋转轴误差会影响加工精度和在机测量结果,本文研究了旋转轴误差的在机测量与建模方法。介绍了基于标准球和机床在机测量系统的旋转轴综合误差测量方法,采用随机Hammersely序列分组规划旋转轴的测量角位置,通过自由安放策略确定标准球初始安装位置。然后,引入模糊减法聚类和模糊C-均值聚类(Fuzzy C-means,FCM)建立旋转轴误差的径向基(Radial basis function,RBF)神经网络预测模型。最后,进行数学透明解析,从而为误差的精确解析建模提供新途径。利用曲面的在机测量实例验证了提出的旋转轴误差测量与建模方法。结果表明:利用所建模型计算的预测位置与实测位置的距离偏差平均值为9.6μm,最大值不超过15μm;利用所建模型补偿工件的在机测量结果后,其平均值由32.5μm减小到13.6μm,最大误差也由62.3μm减小到18.6μm。结果显示,提出的测量方法操作简单,自动化程度高;模糊RBF神经网络的学习速度快、适应能力强、鲁棒性好,能满足高度非线性、强耦合的旋转轴误差建模要求。     

图像式双正弦条码高程定位方法研究

提出了一种用双正弦条码标尺代替传统刻度尺，并通过光电成像和图像处理技术实现远距离高程测量中标尺自动化读数的方法。基于两正弦信号的相位差与标尺位置一一对应的关系，采用相位法经傅里叶频谱分析解码和精定位两步计算标尺高度值，测量误差取决于条码中心定位。实验结果表明，该方法可行且能保证解码的正确性。