Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle

Abstract

Thermal error is one of primary reasons affecting the cutting accuracy of a machine tool. Especially, high-speed spindle’s thermal error restricts the improvement of high-precision machine tool accuracy. In this paper, the traditional back propagation (BP) neural network modeling principle is firstly analyzed. Then, considering the BP network, which is simple and adaptive but converges slowly and is easy to reach local minima, a genetic algorithm (GA) is introduced to optimize BP network’s initial weights and thresholds. Lastly, the network is trained using combined GA and BP technologies with practical thermal error sample data. In experiments of thermal error prediction of the high-speed spindle in a machine tool, the average compensation rate is increased from 89.03 % of BP model to 93.155 % of GABP model. Therefore, GABP model shows its effectiveness in quickly solving the global minimum searching problem.