Study on Dynamic Error Compensation Algorithm of Coordinate Measuring Machine with Flexible Arm
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摘要: 针对柔性臂坐标测量机误差因素复杂且误差影响之间呈非线性的问题，分析了误差因素并对部分动态误差进行研究，提出了一种基于模拟退火和神经网络的柔性臂坐标测量机动态误差补偿方法。利用BP神经网络建立动态误差补偿模型，通过模拟退火算法优化权值从而解决了神经网络的收敛速度慢的问题。通过实验获得数据样本，训练所建模型后对测试数据进行误差补偿。与BP神经网络模型进行对比结果表明，补偿测试点后得出的单点重复性测量误差提高了60.85%，长度测量误差的精度提高了54.79%，证明了所提方法的有效性和可行性。Abstract: In order to solve the complex error factors and further improve the precision of the coordinate measuring machine with flexible arm, the measurement error factors are analyzed and the dynamic errors are studied. A dynamic error compensation method of coordinate measuring machine with flexible arm is proposed based on simulated annealing and neural network. The BP neural network is used to establish a dynamic error compensation model, and the simulated annealing algorithm is used to optimize the weight to solve the problem of slow convergence of the neural network. The data samples are obtained through experiments, and the test data is trained to compensate the error. Comparing with the BP neural network model, the results show that the single point repeatability measurement error is improved by 60.85%, and the accuracy of the length measurement error is improved by 54.79%, which proves the validity and feasibility of the proposed method.
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表 1 结构参数的标称值
关节 θ/rad α/rad l/mm d/mm 1 0 -90 20 0 2 -1.3 90 -20 0 3 -2.1 -90 16 460 4 -3.56 90 -16 0 5 -0.6 90 16 360 6 -2.1 90 -16 0 
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表 3 BP和SA-BP模型误差补偿对比
项目 最大值 最小值 平均值 单点重复性误差 误差补偿前 0.175 83 0.061 26 0.092 26 BP模型 0.108 27 0.030 41 0.052 64 SA-BP模型 0.069 98 0.022 25 0.036 11 长度测量误差 误差补偿前 0.156 24 0.053 73 0.082 51 BP模型 0.086 65 0.029 76 0.045 56 SA-BP模型 0.062 03 0.021 14 0.037 35
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