Dynamic Error Compensation Model of Articulated Arm Coordinate Measuring Machine

Abstract

: 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.