文档介绍：上海交通大学
硕士学位论文
基于切削声音信号的刀具状态识别研究
姓名:谢政
申请学位级别:硕士
专业:机械制造及自动化
指导教师:邵华
20080101
上海交通大学工学硕士学位论文摘要
基于切削声音信号的刀具状态识别研究

摘要
刀具磨损状态的精确监控是保证金属切削加工过程顺利进行的关
键,因此研制准确、可靠且成本低廉的刀具磨损状态监控系统一直是研
究人员所追求的目标。在众多刀具状态监控方法中,切削声音信号检测
技术,以其信号直接来源于切削区,具有灵敏度高、响应快的优点,非
常适用于刀具磨损监控。同时与高频声发射信号(AE)相比,切削声音
信号相对频率较低,相应的采集装置成本低廉,结构简单,且传声器安
放位置可调整,满足监控系统实际生产应用的要求。但是应注意的是,
由于切削声音信号频率低,非常容易受到环境噪声、机床噪声等因素的
干扰。如何获取高信噪比的刀具状态声音特征是该监控技术的关键。
本文首先建立了切削声音信号的采集系统,并通过试验获取了刀具
在不同磨损状态下的切削声音信号。基于切削声音信号的时域统计分析
和频域功率谱分析,发现时域统计特征均方值与刀具磨损状态有明显对
应关系,与刀具磨损相关的特征频率段为 2kHz~3kHz。论文还通过试验
研究了不同主轴转速、进给速率对刀具磨损状态的影响。基于小波分析,
首先将声音信号分为八个不同的频率区间,并以不同频率区间段能量占
信号总能量的百分比作为识别刀具磨损状态的特征向量。然后探索了基
第 I 页
上海交通大学工学硕士学位论文摘要
于 BP 神经网络的刀具状态识别方法。最后在 LABVIEW 环境下调用 MATLAB
神经网络程序,初步实现了刀具磨损的识别。

关键词:刀具磨损,切削声音信号,刀具磨损监控系统,小波分析,
神经网络

第 II 页
上海交通大学工学硕士学位论文摘要
Research on Tool Wear Recognition Based on Cutting Sound Signals
ABSTRACT
The precise monitoring of the tool wear condition is the key to guarantee
the metal cutting procedure. There is always the researchers’ target that how
to develop an exact, reliable and low cost tool wear monitor system.
Compared with the other tool wear monitor technologies, the cutting sound
signal analysis technology is more suitable to the tool wear monitor because
it has many merits such as high sensitiveness, respond quickly and so on.
pared with the acoustic emission signal, the cutting sound
signal has lower frequency and the low cost acquisition devices as well as
the set up location can be adjustable. But there are also many problems
should be resolved, for example the environmental and machine tool
disturbances. According to those reasons, this paper hopes to research how
to avoid the defects and utilize cutting sound signal’s merits and eventually
establish a simple, reliable and low cost tool flank wear monitor and
recognize system.
Firstly, we established an experimental monitor system