High-speed spindle thermal error modeling based on genetic algorithm and BP neural network

To avoid the disadvantages of thermal error modeling method based on Multiple Input Multiple Output-Back Propagation neural network (MIMO-BP) such as excessive dependence on training samples and poor convergence and worse generality, the gray cluster grouping and correlation analysis were used to group temperature variables and optimize thermal senstive points. Subsequently, a Genetic Algorithm (GA) which regarded the absolute value sum's reciprocal of differences between predictive and desired outputs as the number of nodes in hidden layer was used to optimize the topology of MIMO-BP network. The thresholds and weights of network were optimized by setting the residual error limits of output layer. The elongation and thermal tilt angle models were established based on MIMO-BP and GA-BP modeling. The five-point method was utilized to measure the spindle thermal errors of jig-boring, and the effectiveness of the measurement and modeling was validated by the experiment results. The GA-BP model could predict thermal errors under different cutting conditions, and it was more suitable as the thermal error compensation model.