基于参数策略的改进粒子群优化PNN神经网络刀具磨损研究

Research on Improved Particle Swarm Optimization PNN Neural Network Tool Wear Based on Parameter Strategy

刀具磨损直接影响工件加工质量和尺寸精度,正确掌握刀具磨损状态及时换刀,减少机床停机时间,将直接提高加工效率。为提高刀具磨损状态识别准确率,提出一种基于参数策略的改进粒子群优化PNN(IPSO-PNN)神经网络识别刀具的磨损状态。相较于BP神经网络收敛速度慢、易陷入局部最优的缺点,IPSO-PNN神经网络结构简单、训练简洁快速。与BP神经网络和标准PNN神经网络仿真结果对比,结果表明:IPSO-PNN神经网络识别准确率高,收敛速度快,仿真耗时短,能有效提高刀具磨损识别准确率。

Tool wear directly affects the machining quality and dimensional accuracy of the workpiece. Correctly grasping the tool wear status and changing tools in time will reduce machine downtime, directly improve processing efficiency. In order to improve the accuracy of tool wear state recognition, an improved particle swarm optimization PNN(IPSO-PNN) neural network based on parameter strategy was presented to identify the tool wear state. BP neural network has the disadvantage of slow convergence speed and easy to fall into local optimal.Compared with BP neural network, IPSO-PNN has simple structure and simple and fast training. Compared with the simulation results of BP neural network and standard PNN neural network, the results show that IPSO-PNN neural network has high recognition accuracy, fast convergence speed and short simulation time. It can effectively improve the accuracy of tool wear recognition.