摘要
切削加工颤振是一种自激振动,会导致切削质量下降,造成机床和刀具损害。在线监测技术是避免颤振发生的有效手段,可以为颤振抑制提供基础信息。该技术包括动态信号采集、有效特征量提取、颤振状态识别三个环节。本文基于以上环节展开论述了接触式、非接触式信号采集;时域、频域、时频域下特征量提取模式和混沌特征指数;神经网络、隐马尔可夫模型、支持向量机在颤振在线监测技术中的应用。综上,通过总结目前在线监测技术的特点,以期未来在智能制造的驱动下,该技术能持续优化和发展。
Cutting chatter is a kind of self-excited vibration that can lead to a decline in cutting quality and cause damage to machine tools and tools.Online monitoring technology is an effective means to avoid chatter and can provide basic information for chatter suppression.This technology includes three main steps:dynamic signal acquisition,effective feature extraction,and chatter state identification.This article provides a comprehensive review of contact and non-contact signal acquisition methods,feature extraction modes in the time domain,frequency domain,and time-frequency domain,as well as chaotic characteristic indexes.The article also explores the applications of neural networks,hidden Markov models,and support vector machines in chatter online monitoring technology.By summarizing the characteristics of current online monitoring technologies,it is hoped that under the driving force of intelligent manufacturing,this technology can continue to be optimized and developed in the future.
作者
袁野
Yuan Ye(Tianjin Railway Technical and Vocational College,Tianjin,China,300240)
出处
《仪器仪表用户》
2024年第9期76-78,81,共4页
Instrumentation
关键词
切削颤振
在线监测
信号采集
特征提取
颤振识别
cutting chatter
online monitoring
signal acquisition
feature extraction
chatter identification
