Modeling of Measurement Space Error of AACMM

The error sources of AACMM were many and complex, and the measurement space error was uncertain. In order to obtain the measurement space error of AACMM accurately, the single-point measurement precision experiment of AACMM was carried out by using the standard cone. According to the experiment, also obtained the training data sample and test data sample. The measurement space error model of AACMM was built up by BP neural network, and the particle swarm optimization algorithm (PSO) was introduced to optimize the convergence speed and the operation speed of BP neural network. The prediction and verification of the model was carried out. Results show that BP neural network and PSO-BP neural network both can predict the measurement space error, the prediction results of PSO-BP neural network model are more accurate, and the relative error is smaller.