文章摘要
李健,樊妍,何斌.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[J].机床与液压,2021,49(3):75-80.
LI Jian,FAN Yan,HE Bin.Research on Improved Particle Swarm Optimization PNN Neural Network Tool Wear Based on Parameter Strategy[J].Machine Tool & Hydraulics,2021,49(3):75-80
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
  
DOI:10.3969/j.issn.1001-3881.2021.03.015
中文关键词: 刀具磨损  状态识别  IPSO-PNN神经网络  BP神经网络
英文关键词: Tool wear  State recognition  IPSO-PNN neural network  BP neural network
基金项目:国家自然科学基金项目(61825303)
作者单位E-mail
李健 陕西科技大学电子信息与人工智能学院 1224579458@qq.com 
樊妍 陕西科技大学电子信息与人工智能学院 1239651452@qq.com 
何斌 同济大学电子与信息工程学院  
摘要点击次数: 81
全文下载次数: 0
中文摘要:
      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。
英文摘要:
      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.
查看全文   查看/发表评论  下载PDF阅读器
关闭

分享按钮