电子测量与仪器学报

魏舜昊,章家岩,冯旭刚.三坐标测量机高速测量过程动态误差分析与补偿[J].电子测量与仪器学报,2020,34(5):43-50

三坐标测量机高速测量过程动态误差分析与补偿

Dynamic error analysis and compensation of CMM high speed measurement

DOI：

英文关键词:coordinate measuring machines dynamic error fuzzy neural network error compensation

基金项目:安徽省自然科学基金( 1908085ME134)、安徽省重点研究与开发计划( 1804a09020094)、安徽省高校自然科学研究重点项目(KJ2018A0060)资助

作者	单位
魏舜昊	1.安徽工业大学电气与信息工程学院
章家岩	1.安徽工业大学电气与信息工程学院
冯旭刚	1.安徽工业大学电气与信息工程学院

Author	Institution
Wei Shunhao	1.School of Electrical and Information Engineering, Anhui University of Technology
Zhang Jiayan	1.School of Electrical and Information Engineering, Anhui University of Technology
Feng Xugang	1.School of Electrical and Information Engineering, Anhui University of Technology

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中文摘要:

三坐标测量机测量过程的动态误差制约着工业现场测量效率的提高,为此,提出一种三坐标测量机高速测量过程动态误差补偿方法以改善测量精度。为使研究具备典型性与代表性,以市场上较为广泛使用的移动桥式三坐标测量机为研究对象, 通过建立误差分离平台分析测量机高速测量过程动态特性,确定了能够表征测量过程动态误差的四项参数,即最大定位误差 (MPE)、残余定位误差(RPE)、最大逼近误差(MAE)、残余逼近误差(RAE)。采用正交实验方法分析了动态误差参数的共性影响因子(定位速度、定位距离、逼近速度、逼近距离)对动态参数的影响程度,并利用三坐标测量机测量标准球得到训练样本和测试样本,分别使用训练样本和测试样本对测量机测量过程动态误差进行建模和补偿。结果表明,经模糊神经网络模型补偿后动态过程误差分别减小了 88. 8%、80. 2%、90. 8%、71. 3%,证明了模糊神经网络模型能够有效提高测量机的动态测量精度。

英文摘要:

The dynamic error of CMM measurement process restricts the improvement of measurement efficiency in industrial field. For this purpose, a dynamic error compensation method for high-speed measurement process of CMM is proposed to improve measurement accuracy and efficiency. In order to make the research typical and representative, the mobile bridge CMM widely used in the market is taken as the research object. By establishing an error separation platform to analyze the dynamic characteristics of the high-speed measurement process of the measuring machine, four parameters that can characterize the dynamic errors of the measurement process are determined, namely maximum positioning error (MPE), residual positioning error (RPE), maximum approximation error ( MAE), residual approximation error (RAE). Orthogonal experiment method was used to analyze the influence degree of common influence factors (positioning speed, positioning distance, approaching speed, approaching distance) on dynamic parameters. Training samples and test samples were obtained by measuring standard spheres with coordinate measuring machine. The training samples and test samples are used to model and compensate the dynamic errors of the measuring machine. The results show that the dynamic process errors are reduced by 88. 8%, 80. 2%, 90. 8% and 71. 3% respectively after compensated by the fuzzy neural network model. It proves that the fuzzy neural network model can effectively improve the dynamic measurement accuracy of the measuring machine.

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