铰接臂三坐标测量机动态误差补偿模型

Jiaqi Zhu, Xugang Feng, Jiayan Zhang
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摘要

铰接臂三坐标测量机(AACMM)的误差因素很多,而且误差因素之间的关系是非线性的,用传统的数学建模方法难以建立模型。本文分析了误差来源,在参数标定的基础上,选择角度编码、热变形和探头系统作为研究对象,引入坐标值间接描述模型中剩余误差。采用BP神经网络建立误差补偿模型,采用改进的模拟退火(MSA)算法优化神经网络的连接权,解决了神经网络易陷入局部极小值和易受干扰的问题。通过实验获得数据样本,并利用测试数据对所建立的模型进行验证。实验结果表明,补偿后的单点重复性误差平均值由0.1782 mm减小到0.0383 mm。
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Dynamic Error Compensation Model of Articulated Arm Coordinate Measuring Machine
: The error factors of articulated arm coordinate measuring machine (AACMM) are many and the relationship between them is nonlinear, which is difficult to establish the model by traditional mathematical modeling. This paper analyses the error sources, on the basis of parameter calibration, to select the angle coding, thermal deformation and probe system as the research object and introduce coordinate values to indirectly describe the remaining errors in the model. The BP neural network is used to build up the error compensation model, connection weights of the neural network are optimized by the modified simulated annealing (MSA) algorithm, which solves the problem that the neural network is easy to fall into the local minimum and the susceptible to interference. The data samples are obtained through experiments, and the test data are utilized to exercise model built. The experimental result demonstrates that the average value of the single point repeatability error after compensation is reduced from 0.1782 mm to 0.0383 mm.
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