基于小波变换的刀具磨损检测方法

提出采用切削力信号的奇异性指数作为衡量刀具磨损的参量。利用小波变换对切削力信号进行分析 ,变换结果的模极大值点反映了刀具发生磨损或破损的时刻 ,而其奇异性指数的大小则反映了刀具的磨损状况。试验结果表明了该方法的有效性。     

刀具磨损的小波检测

通过建立刀具磨损时的切削力信号奇异性指数与刀具磨损状态的对应关系 ,利用小波变换对切削力信号进行分析 ,实现了对刀具磨损的间接测量     

采用小波神经网络的刀具故障诊断

为了有效的进行刀具状态监测，采用小波神经网络的松散型结合对刀具进行故障诊断。通过小波变换提取刀具磨损声发射（AE）信号的特征．即对AE信号进行小波分解，提取了5个频段的均方根值作为神经网络的输入，来识别刀具磨损状态。试验表明，均方根值完全可以作为刀具磨损过程中产生AE信号的特征向量。仿真结果表明，基于小波神经网络的刀具故障诊断对刀具磨损状态的识别效率高．该方法是有效的。     

刀具磨损声发射信号处理中小波基选取的研究

通过对小波基性质和刀具磨损声发射（AE）信号特点的研究,从理论上分析了小波变换中刀具磨损AE信号处理中小波基选取的方法。在试验验证过程中,根据小波包信号分解遵循能量守恒原理,用四种小波基对刀具磨损AE信号进行三层小波包分解;以AE信号经小波包分解后各频带上的能量为特征参数,比较四种情况下特征参数的变化,验证了理论分析的正确性。     

CPS系统中刀具加工状态实时监测的新方法

机床是信息物理系统(CPS系统)中主要的执行单元和感知单元,对其加工状态的动态监测和实时感知可以提高产品质量。为了实现加工现场信号采集和刀具加工状态在线监测,设计了主轴功率信号采集系统,同时引入力信号作为对比分析,应用希尔伯特-黄变换和小波变换根据特征频率段的信号特征构造了刀具磨损系数,将刀具磨损状态和磨损系数对应起来,在加工现场实现了刀具状态的在线监测。通过和小波变换的对比,证明了希尔伯特-黄变换在处理功率信号方面可以有效抑制噪声信号,提高监测的准确性。     

螺杆包张中铣削过程刀具磨损状态的小波分析

利用异形螺杆包络铣削过程中产生的振动信号,采用小波变换对其进行精确的细分,提取出加工过程中刀具磨损的特征信息,据此分析该加工过程的刀具磨损状况,为刀具磨损的状态检测和实时补偿提供了准确的依据.     

基于小波包分解提取刀具振动信号特征向量

主要介绍了3种基于小波包分解的以不同方式进行提取刀具磨损振动信号特征向量的方法。刀具振动信号通过小波包分解后重构成不同频段的信号系数。在此基础上,首先提取各个频段能量基于总能量比值的特征向量;其次对其进行功率谱分析,提取特定频段幅值的特征向量;最后,利用奇异值分解将不同频段的信号映射到正交子空间中,从中选取信号的奇异值作为特征向量。最终将得到的特征向量组合成一个特征向量输入支持向量机中进行刀具磨损识别。     

数控机床刀具故障在线监测系统开发

介绍小波分析、虚拟仪器及其软件开发平台LabVIEW基本原理的基础上,着重阐述了小波消噪和奇异信号检测在数控机床刀具故障诊断中的应用,以及小波分析与虚拟仪器相结合应用于故障信号分析.设计开发了基于LabVIEW8.2虚拟平台的数控机床刀具故障监测系统,该系统将数据采集,数据分析和故障诊断融为一体,实现机械设备在线检测、实时故障诊断.最后通过数控铣床刀具磨损故障监测实验,验证了该监测系统在工程实际应用中的合理性和实用性.     

基于小波变换的埋地钢质管道外覆盖层缺陷定位技术研究

对小波变换检测信号奇异性理论进行阐述.以DCVG/CIPS组合仪器检测数据为对象,利用小波变换对阴极保护埋地管道通电电位、断电电位和电压梯度检测信号进行处理,根据小波变换模极大值对应着信号奇异点理论,实现了覆盖层缺陷点位置的准确定位.实验表明,该方法定位准确,具有一定的实用性.     

基于小波分析和模糊控制理论的铣刀磨损监控方法

研制了一种铣刀磨损的监控方法.在该系统中信号采集采用声发射传感器,信号的特征提取采用小波分析的方法,将变换后的尺度系数和各个频段的小波系数作为特征,采用自行设计的Sugeno模糊控制系统进行状态识别,模糊控制系统的输出是刀具磨损的具体值.     

Effect of tool edge wear on the cutting forces and vibrations in high-speed finish machining of Inconel 718: an experimental study and wavelet transform analysis

High-speed machining has been receiving growing attention and wide applications in modern manufacture. Extensive research has been conducted in the past on tool flank wear and crater wear in high-speed machining (such as milling, turning, and drilling). However, little study was performed on the tool edge wear??the wear of a tool cutting edge before it is fully worn away??that can result in early tool failure and deteriorated machined surface quality. The present study aims to fill this important research gap by investigating the effect of tool edge wear on the cutting forces and vibrations in 3D high-speed finish turning of nickel-based superalloy Inconel 718. A carefully designed set of turning experiments were performed with tool inserts that have different tool edge radii ranging from 2 to 62???m. The experimental results reveal that the tool edge profile dynamically changes across each point on the tool cutting edge in 3D high-speed turning. Tool edge wear increases as the tool edge radius increases. As tool edge wear dynamically develops during the cutting process, all the three components of the cutting forces (i.e., the cutting force, the feed force, and the passive force) increase. The cutting vibrations that accompany with dynamic tool edge wear were analyzed using both the traditional fast Fourier transform (FFT) technique and the modern discrete wavelet transform technique. The results show that, compared to the FFT, the discrete wavelet transform is more effective and advantageous in revealing the variation of the cutting vibrations across a wide range of frequency bands. The discrete wavelet transform also reveals that the vibration amplitude increases as the tool edge wear increases. The average energy of wavelet coefficients calculated from the cutting vibration signals can be employed to evaluate tool edge wear in turning with tool inserts that have different tool edge radii.     

小波变换对突变信号峰值奇异点的精确检测

研究了小波变换对突变信号峰值奇异点的精确检测机理和方法,采用了离散小波变换的直接算法,避免了塔式算法在本检测方法中的某些局限性。通过模拟算例和应用实例的验证,证明即使在有严重噪声干扰的情况下,该方法也很容易实现对突变信号峰值奇异点的准确检测和精确定位,具有相当高的定位精度（其误差不大于1个采样点）和分析精度（不存在累积误差）,同时具有较高的实时性。     

Acoustic emission method for tool condition monitoring based on wavelet analysis

It is believed that the acoustic emission (AE) signals contain potentially valuable information for tool wear and breakage monitoring and detection. However, AE stress waves produced in the cutting zone are distorted by the transmission path and the measurement systems and it is difficult to obtain an effective result by these raw acoustic emission data. In this article, a technique based on AE signal wavelet analysis is proposed for tool condition monitoring. The local characterize of frequency band, which contains the main energy of AE signals, is depicted by the wavelet multi-resolution analysis, and the singularity of the signal is represented by wavelet resolution coefficient norm. The feasibility for tool condition monitoring is demonstrated by the various cutting conditions in turning experiments.     

A method of using Hoelder exponents to monitor tool-edge wear in high-speed finish machining

Tool condition monitoring is increasingly important as a widespread application of automated, computer numerically controlled machining in a variety of modern industries. Although a significant amount of research on tool condition monitoring in machining has been conducted during the past few decades, the research is primarily focused on tool flank wear. Less attention is paid to tool-edge wear, which is a critical issue in high-speed finish machining where the feed rate is in the same magnitude as tool edge dimensions, and thus, the tool cutting edge is subjected to extensive mechanical and thermal deformation. The present study fills this important research gap in tool condition monitoring. This paper presents a method of monitoring tool-edge wear in the high-speed finish machining of an aerospace superalloy Inconel 718 by extracting Hoelder exponents from wavelet transform analysis of cutting vibrations. A total of 60 cutting experiments were conducted, covering a range of cutting speed and feed rate conditions. The experimental results show that cutting vibrations increase as tool-edge wear develops. Wavelet transform analysis can be employed to identify single local maxima of the cutting vibration signals. As tool-edge wear develops, the values of Hoelder exponents vary from 0.55 to 0.90. It is suggested that under the cutting conditions tested in the present study, 0.8 can be used as the threshold value of Hoelder exponents to differentiate severe and normal tool-edge wear.     

Intelligent monitoring and prediction of tool wear in CNC turning by utilizing wavelet transform

In order to realize an intelligent CNC machine, this research proposed the in-process tool wear monitoring system regardless of the chip formation in CNC turning by utilizing the wavelet transform. The in-process prediction model of tool wear is developed during the CNC turning process. The relations of the cutting speed, the feed rate, the depth of cut, the decomposed cutting forces, and the tool wear are investigated. The Daubechies wavelet transform is used to differentiate the tool wear signals from the noise and broken chip signals. The decomposed cutting force ratio is utilized to eliminate the effects of cutting conditions by taking ratio of the average variances of the decomposed feed force to that of decomposed main force on the fifth level of wavelet transform. The tool wear prediction model consists of the decomposed cutting force ratio, the cutting speed, the depth of cut, and the feed rate, which is developed based on the exponential function. The new cutting tests are performed to ensure the reliability of the tool wear prediction model. The experimental results showed that as the cutting speed, the feed rate, and the depth of cut increase, the main cutting force also increases which affects in the escalating amount of tool wear. It has been proved that the proposed system can be used to separate the chip formation signals and predict the tool wear by utilizing wavelet transform even though the cutting conditions are changed.     

小波分析用于磁场刺激诱发肌电信号的实时检测

肌电信号是在皮肤表面记录下来的神经和肌肉的系统活动时的生物电信号 ,有很好的临床价值。文章着重介绍应用小波分析中的奇异点探测理论 ,对磁场刺激下的诱发肌电信号进行分析 ,实时地提取磁场刺激的诱发肌电信号特征。验证了小波分析理论在分析非平稳的随机信号的优越性 ,是信号分析的又一有力工具。     

铣削加工中心对刀方案及刀具长度补偿措施

数控程序控制机床用刀具对工件加工之前,不仅要定义刀具参考基准相对工件的位置,还必须定义刀具刀位点相对工件的位置。文章分析了不同对刀方案的对刀原理及操作方法、刀具长度补偿措施及应用特点。