基于改进PSO优化BP的数控机床热误差预测研究

Thermal Error Prediction of Numerical Control Machine Based on Improved Particle Swarm Optimized Back Propagation Neural Network

热误差是影响数控机床加工精度的主因,为提高数控机床热误差模型的预测精度,提出了基于改进粒子群优化BP神经网络的数控机床热误差建模预测方法。针对BP易陷入局部最优、收敛速度慢,在标准粒子群算法的基础上,改进粒子的速度与位置更新策略,在此基础上优化BP神经网络的阈值和权值,并建立数控机床热误差预测模型;借助于MATLAB完成仿真实验,结果表明,与标准的BP神经网络和支持向量机相比,基于改进粒子群优化BP神经网络的数控机床热误差预测模型精度高、泛化能力强。

Thermal error is a primary factor affecting the working accuracy of numerical control machine,whereas it can＇t be measured on-line. So an accurate predictive error is critical to final product quality. In order to improve the predictive accuracy of thermal error,a predictive method based on improved particle swarm optimized back propagation neural network is proposed in this paper. Because of back propagation neural network has the disadvantages of low convergence rate,easy to fall into local optimization and so on,an improved updating strategy of particle position based on the stand particle swarm method is built. Then with the improved particle swarm algorithm,the threshold and weight of the neural network is optimized to form the thermal error predictive method. The simulation results conducted on MATLAB shows that the proposed thermal error predictive method has a higher predictive accuracy,better generalization ability compared with stand BP neural network and support vector machine.