Abstract: Joint angle errors have important influence on the accuracy of articulated arm coordinate measuring machines (AACMMs). There are many factors causing joint angle errors, so it is difficult to describe them accurately with mathematic models. A compensation method based on radial basis function (RBF) neural networks is presented to solve the problem. A practical method to calibrate errors of joint angles with a coordinate measuring machine is designed, thereafter the angle errors of six joints of an AACMM are calibrated discretely. The calibration data is used to train the RBF neural networks, after training the RBF neural networks are used to compensate the angle errors of six joints respectively. Experimental tests are carried out to evaluate the efficiency of the RBF neural network compensation method, showing that after compensation the rotation accuracy of the joints angle is improved greatly, and the RBF neural network compensation method is more efficient than a compensation model of sine function, therefore, it is valuable for engineering applications.

Key words: Angle measurement, Articulated arm coordinate measuring machine, Calibration, Error compensation, Radial basis function neural networks