Study on Machine Accuracy of the Serial-Parallel Machine Tool Based on the BP Neural Network

It were researched that the modeling methods of machine accuracy and the control techniques of the error compensation based on BP neural network(BPNN) for parallel machine tool(PMT)with five degrees of freedom(DOF). The samples are obtained to train the BP neural network which has good capacity for non- liner mapping, learning and generalization. The machine accuracy mathematics model is established for the error compensation, in order to study the nonlinear input and output problem of the parallel machine which difficultly modeling described. The trained neural network was applied to error compensation of PMT to realize modifying errors real-timely. Finally, simulation analysis was performed through the MATLAB software. The results expressed that the control strategies for error compensation were simple, efficient and practicable. Machine accuracy can be increased greatly after compensation.