频带能量特征法在声发射刀具磨损监测系统中的应用

基于对声发射(AE)信号特点的分析和小波包分解理论对不平稳信号特征提取的优势,提出一种利用AE信号的能量变化来监测刀具磨损状态的方法。该方法利用db8小波基对AE信号进行5层小波包分解,将分解后各频带上的能量值作为特征参数,并组成特征向量。分别提取在新刀和刀具磨损状态下的特征向量,根据其变化即可判别刀具磨损的程度。试验结果验证了该方法在刀具磨损判析中的可用性。     

刀具磨损状态检测方法的分析与验证

为监测机床刀具磨损程度,提出了一种基于小波包理论(WPD)、经验模态分解(EMD)以及支持向量机(SVM)等相结合的刀具故障诊断方法。通过小波包理论工具消除刀具的高频噪声信号,并对去噪后的信号进行模态分解、合成,计算出模态函数(IMF)和EMD分解信号的相关参数。将计算出的信号时域上的特征参数作为支持向量机(SVM)的输入特征向量,完成对刀具故障的检测。实验结果分析表明,该方法可以有效地判断刀具磨损程度,验证了方法的可行性。     

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。     

基于最佳小波基小波特征的神经网络刀具磨损状态监测方法

以小波分析理论为基础,提出了以对数熵理论确定最佳小波包分解树结构的方法,提出了基于声发射信号最佳小波基最佳小波分量频段能量的声发射信号小波特征,开发了基于最佳小波基小波特征的神经网络刀具磨损状态在线监测系统,实验结果表明,该系统具有较高的监测精度,能满足工业现场对刀具磨损状态实时在线监测的要求.     

基于云理论与LS-SVM的刀具磨损识别方法

针对刀具磨损过程中产生声发射信号的不确定性以及神经网络学习算法收敛速度慢、易陷入局部极小值、对特征要求较高等问题,提出了基于云理论和最小二乘支持向量机的刀具磨损状态识别方法。首先,对声发射信号进行小波包分解与重构,滤除干扰频段对求取特征参数的影响;其次,对重构后的信号利用逆向云算法提取云特征参数:期望、熵、超熵,分析刀具磨损声发射信号的云特性及磨损状态与云特征参数之间的关系;最后,将云特征参数组成特征向量送入最小二乘支持向量机进行识别。研究结果表明:所提取的特征可以很好地反映刀具的磨损状态,云-支持向量机方法可以有效地实现刀具磨损状态的识别,与传统神经网络识别方法相比具有更高的识别率,识别率达到96.67%。     

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。     

基于WPD_EMD和SVM刀具磨损故障诊断模型

通过采集2种磨损程度不同的同类型刀具加工工件时机床主轴的振动信号,提出WPD_EMD和SVM故障诊断模型判断刀具磨损程度。首先利用小波包工具去除高频噪声信号,其次利用EMD分解得到若干个固有模态函数和一个残差,计算各个固有模态函数和EMD分解前信号的相关系数,合并相关系数大的固有模态函数得到新信号。计算新信号的绝对均值作为时域特征参数。选取若干组试验数据作为支持向量机训练集,建立判断刀具磨损程度大小的故障诊断模型。试验表明该故障模型预测刀具磨损程度准确率100%,为判断刀具实时加工工件的磨损程度提供新的途径。     

基于小波包分析和LS-SVM的钻削刀具状态识别研究

为了有效地识别钻削刀具磨损状态,提出一种基于小波包分析和最小二乘支持向量机(LS-SVM)的状态识别方法。通过在线监测钻削加工过程中的钻削轴向力和刀具状态,采用时域分析、频域分析以及小波包分析法对刀具磨损状态的信号进行特征向量提取,建立基于最小二乘支持向量机(LS-SVM)的分类识别模型。通过试验验证了该方法可以提高刀具磨损状态的识别精度。     

基于MIC和改进Bagging-GPR的刀具磨损预测

为实现刀具磨损的准确预测，对加工过程的换刀和参数优化提供指导，提出一种基于最大信息系数(MIC)和改进的Bagging集成高斯过程回归(Bagging-GPR)的刀具磨损预测方法，建立切削力信号与刀具磨损间的非线性映射关系。采集加工的切削力信号，运用时域、小波包分解和经验模态分解提取切削力信号特征，并利用MIC分析特征与刀具磨损的相关度来实现特征选择，避免预测模型的“维数灾难”。为提高预测模型的精度，考虑高斯子模型内部核函数的差异性及准确性，利用Bagging对高斯核函数进行随机组合，作为各子模型的核函数，构建改进的Bagging-GPR模型实现刀具磨损值预测，并基于铣削实验数据验证了所提方法的有效性和优异性。     

基于多特征量的刀具磨损模糊判决研究

利用信号处理技术分析AE、Ac及切削力信号特征参数随刀具磨损的变化规律,并将这些特征参数组成反映刀具磨损状态的特征矢量。论文提出基于模糊理论的刀具磨损状态识别方法。试验证明,该方法能有效地判断刀具状态,比常用的利用神经网络进行磨损状态分析的方法更具有理论直观性与操作的时效性。     

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.     

模糊数据融合的刀具磨损状态智能识别

为实现高速加工时刀具渐变磨损状态的在线准确识别,提出了一种集合多种智能的间接检测刀具磨损状态方法的模糊数据融合方法。尽管这些方法具有算法实现较为简单、处理速度较快的优点,但单一的信号检测及单一的智能建模方法难以获得全面的加工状态信息和准确的识别结果。为此,利用F推理技术对上述方法的冗余和互补信息进行数据融合,应用Makino—Fanuc 74-A20型加工中心的测试数据验证了该方案的可行性,并将刀具后刀面磨损的预测值与基于机器视觉检测的实测值进行比较。实验结果分析表明,多参数模糊融合识别方法能快速获得切削刀具磨损状态更加准确的预测值。     

基于声发射技术监测刀具磨损的研究

刀具磨损的研究方法很多,本文针对近些年发展的声发射技术(AE)在监测刀具磨损上的应用,采用理论分析和现场试验的方法进行可行性分析和验证,结果表明:在考察的几个影响声发射信号强度的因素之中,刀具主切削刃后刀面磨损量对其影响最为显著,这为利用AE技术研究刀具磨损提供了可行性依据;通过对铣刀AE信号进行时域振铃分析,清晰再现了刀具在不同时刻的磨损情况。     

Model development for tool wear effect on AE signal generation in micromilling

A model for the relation between the acoustic emission signal generation and tool wear was established for cutting processes in micromilling by considering the acoustic emission (AE) generation and propagation mechanisms. In addition, the effect of tool wear on the AE signal generation in frequency and amplitude was studied. In the model development, the finite element analysis was first used to calculate the shear strain rate distribution on the shear plane based on the orthogonal cutting assumption. Conversely, the contact stress distribution of workpiece on the flank wear face was established based on the Waldorf model. Following the finite element method, the dislocation density in materials was calculated based on Orowan’s law with the calculated stress rate. Finally, the AE signal detected by the sensor was calculated by considering the Gaussian probability density function for the distribution of AE source on the shear plane and the one-dimension wave equation for AE signal propagation. Based on the developed model, the effect of tool wear on the AE signal generation was investigated and compared to the experimental results. The results obtained from these investigations indicate that the proposed model can be used to predict the effect of tool wear on the AE signal generation.     

A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals

Tool condition monitoring, which is very important in machining, has improved over the past 20 years. Several process variables that are active in the cutting region, such as cutting forces, vibrations, acoustic emission (AE), noise, temperature, and surface finish, are influenced by the state of the cutting tool and the conditions of the material removal process. However, controlling these process variables to ensure adequate responses, particularly on an individual basis, is a highly complex task. The combination of AE and cutting power signals serves to indicate the improved response. In this study, a new parameter based on AE signal energy (frequency range between 100 and 300 kHz) was introduced to improve response. Tool wear in end milling was measured in each step, based on cutting power and AE signals. The wear conditions were then classified as good or bad, the signal parameters were extracted, and the probabilistic neural network was applied. The mean and skewness of cutting power and the root mean square of the power spectral density of AE showed sensitivity and were applied with about 91% accuracy. The combination of cutting power and AE with the signal energy parameter can definitely be applied in a tool wear-monitoring system.     

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.     

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.     

Tool State Detection by Harmonic Wavelet and Sample Entropy

It is a fact that 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,it is difficult to obtain a reliable result by these raw AE data.It is generally known that the process of tool wear belongs to detect weak singularity signals in strong noise.The objective of this paper is to combine Newland Harmonic wavelet and Richman-Moorman(2000) sample entropy for detecting weak singularity signals embedded in strong signals.First,the raw AE signal is decomposed by harmonic wavelet and transformed into the three-dimensional time-frequency mesh map of the harmonic wavelet,at the same time,the contours of the mesh map with log space is induced.Second,the profile map of the three-dimensional time-frequency mesh map is offered,which corresponds to decomposed level on harmonic wavelets.Final,by computing sample entropy in each level,the weak singularity signal can be easily extracted from strong noise.Machining test was carried out on HL-32 NC turning center.This lathe does not have a tailstock.Tungsten carbide finishing tool was used to turn free machining mild steel.The work material was chosen for ease of machining,allowing for generation of surfaces of varying quality without the use of cutting fluids.In turning experiments,the feasibility for tool condition monitoring is demonstrated by 27 kinds of cutting conditions with the sharp tool and the worn tool,54 group data are sampled by AE.The sample entropy of each level of wavelet decomposed for each one of 54 AE datum is computed,wear tool and shaper tool can be distinguished obviously by the sample entropy value at the 12th level,this is a criterion.The proposed research provides a new theoretical basis and a new engineering application on the tool condition monitoring.     

基于小波多分辨率分析和RBF神经网络的刀具磨损状态识别

刀具磨损监测对于提高加工过程的精度和自动化程度具有重要意义。本文提出一种基于RBF函数神经网络的刀具磨损状态监测模式。该系统利用声发射传感器对切削过程进行监测,采用多分辨率小波分解技术从声发射信号中提取反映刀具磨损的特征向量,并输入RBF神经网络,实现了刀具磨损的自动识别。