ELM在机床切削刀具磨损快速检测中的应用  

Research on Application of Extreme Learning Machine in Rapid Detection of Cutting Tool Wear in Machine Tools

作　　者：唐鑫 巫茜[2] 邝茜 王成睿 Tang Xin;Wu Qian;Kuang Xi;Wang Chengrui(Dingzhao(Chongqing)Packaging Technology Co.,Ltd.,Chongqing 400050,China;College of Computer Science&Engineering,Chongqing University of Technology,Chongqing 400054,China)

机构地区：[1]鼎兆(重庆)包装科技有限公司,重庆400050 [2]重庆理工大学计算机科学与工程学院,重庆400054

出　　处：《兵工自动化》2021年第12期55-59,共5页Ordnance Industry Automation

基　　金：重庆市科技局重点项目(cstc2019jscx-fxydX0047);重庆市科技局重点项目(cstc2019jscx-fxydX0090)。

摘　　要：为检测加工过程中切削刀具的磨损和破损,探讨一种基于声音识别的超限学习机(extreme learning machine,ELM)模型检测方法。论述切削声音信号的时频域特性,讨论基于小波包分解的刀具工作状态敏感频谱能量统计特征量提取方法,构建基于声音特征量识别的ELM快速检测模型。以某操作现场刀具切削磨损声音信号识别实验为例,实测数据验证了采用该模型可获得更高的检测准确度且响应速度更快。实验仿真结果表明:采用ELM模型借助声音识别检测切削刀具磨损的方法是有效的。In order to detect the wear and breakage of cutting tools during machining,an extreme learning machine(ELM)model detection method based on sound recognition was proposed.The time-frequency domain characteristics of cutting sound signal were discussed,and the extraction method of cutting tool status-sensitive spectrum energy statistical feature quantity based on wavelet packet decomposition was discussed.A fast ELM detection model based on sound feature quantity recognition was constructed.An example was taken for the identification of cutting wear sound signal in an operation site.The measured data verify that the proposed model can obtain higher detection accuracy and faster response speed.The experimental simulation results show that the ELM model is effective in detecting cutting tool wear with sound recognition.

关 键 词：切削刀具破损 声音识别 时频特性 小波包分解 ELM检测模型 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]