基于声信号小波分析的刀具破损监测

提出了基于切削声信号的刀具破损监测方法。通过对破损声信号进行小波分析,提取出了与刀具破损具有相应关系的特征频带,去除了冲击声信号、刀具切入声信号等与刀具破损具有相似特征的声信号干扰,通过设定合适的阈值,能够较好地监测刀具的破损。这种监测方法为刀具的破损监测提供了一种新的途径。     

刀具磨损声谱特征主要集中频段研究

可听阈内的切削声信号包含着丰富的刀具磨损信息。找出刀具磨损信息在可听阈内的主要集中频段能提高切削声信号的信噪比,改善识别结果。在提取了可听阈内切削声信号包含的刀具磨损信息的有效特征,建立了识别模型和对刀具磨损状态进行了级别划分的基础上,对不同频段切削声信号展开了识别实验。实验分析结果表明可听阈内刀具磨损信息主要集中在低频段。     

断续车削力信号特性分析

以车削实验为基础，选择切削力作为监测信号，根据时域信息分析了刀具在断续车削条件下的受力特点，采用傅立叶变换和选带傅立叶变换深入分析了切削力的频域信息，并将刀具破损前后的三向力的功率谱进行分析对比。研究表明：径向力Fy的2500～3100Hz频带的功率谱幅值与刀具破损状态及其变化规律密切相关，可根据其特征变化用于刀具的实时监测。     

精密孔加工中声发射信号自动检测刀具对中的新方法

本文介绍在对精密孔进行车削，镗削加工过程中，使用声发射技术自动检测刀具对中的方法及为此建立的刀具准确，灵敏对中的声发射检测实验系统；研究了切削加工中对声发射信号特征参量大小的影响因素，得出了ＡＥ信号参量和刀具与工件孔间偏心量的关系，文章同时介绍了新研制的专用对中仪。     

基于HPSO优化BP神经网络的刀具磨损状态识别

针对BP神经网络容易陷入局部极值导致识别精度低的问题,文章提出了一种基于混合粒子群算法(HPSO)的BP神经网络优化算法。在刀具磨损监测实验过程中,采集刀具切削的声发射(AE)信号,利用小波包分解算法对AE信号进行滤波,并进行特征提取。将频带能量特征和切削参数分别作为主特征和辅助特征,并对其对归一化处理。采用混合粒子群优化算法(HPSO)对BP神经网络预测模型进行优化,利用优化后的模型对测试样本进行模式识别,结果表明,优化后的HPSO-BP模型能够有效地降低神经网络陷入局部极值的情况,提高刀具磨损识别精度。     

基于声信号HMM的刀具磨损程度分级识别

为有效地实时在线监测刀具的磨损状态，提出了基于声音识别技术的刀具磨损监测方法，进行了基于切削声信号HMM的刀具磨损程度的分级识别，监测系统能够对刀具的五级磨损划分进行准确识别，这为刀具的磨损监测提供了一条切实可行的途径。     

刀具实时健康监测系统研究

为保证加工过程中工件加工质量,降低废品率,须实时监测刀具的健康状态。分析了刀具声发射检测机理,设计了一非接触式光纤F-P传感器,建立基于光纤声发射技术的刀具实时监测系统,利用虚拟仪器技术开发了切削加工过程中刀具监测的声发射信号采集和报警软件,进行了实验研究。结果表明,该系统能有效实现刀具切削过程中状态变化的非接触监测,系统软件界面友好,操作简单,具有一定的灵活性,易于扩展功能,可靠性高,成本低。     

基于声发射和神经网络的数控机床刀具故障诊断

分析了数控刀具的切削状态,介绍了声发射检测系统和神经网络技术,对刀具切削状态信息声发射检测的可行性和神经网络技术智能诊断方法进行了分析,并通过数控机床刀具故障诊断实例,验证了通过声发射提取刀具切削状态方法的有效性和通过神经网络智能诊断技术检测刀具切削状态方法的正确性。     

基于声发射的铣刀破损信号特征的提取与优化

铣刀破损监测对实现加工自动化具有重要的意义.提出了基于小波变换的铣刀声发射破损特征提取与优化方法.首先,采用小波变换对铣刀声发射信号进行多分辨率分解,然后提取各频段子信号的能量比作为刀具破损监测的特征量.通过对正常切削、随机冲击和刀具破损这三类信号的比较分析,证明了该特征提取方法能够有效地反映刀具状态.最后,通过正交统计,分析了切削速度、进给速度和切削深度对特征量的影响,并对特征量进行优化.     

刀具磨损监测的一种数据处理方法

采用声发射传感器采集刀具切削时的信号,提出了一种基于BP神经网络识别刀具磨损程度的方法。该方法将原始声发射信号经高通滤波后直接输入到BP神经网络中进行训练,依靠神经网络的非线性映射能力,使神经网络对不同磨损程度刀具产生的信号进行分类,并能准确判别未知信号所属类别。与传统方法相比,该方法省去了人工提取特征值这一费时费力的环节。研究了神经元个数对神经网络的训练和识别的影响,提高了神经网络的识别精度。实验结果表明,该方法可以准确地预测刀具磨损程度。     

基于独立分量分析理论的大块非晶合金切削信号检测

研究了在大块非晶合金切削力信号检测时利用独立分量分析法对检测信号进行去噪处理技术。在试验中，采用独立分量分析法对切削测量系统测量的大块非晶合金切削力信号进行迭代分离，从而提取出主切削力信号。并针对大块非晶合金在不同切削深度下的变形特征，运用扫瞄式电子显微镜观察了大块非晶合金的切削带特征。主切削力信号频谱的快速傅里叶变换分析表明，随着切削深度的增加，切削力信号高频部分的振幅越来越大，而大块非晶合金切削力信号高频部分是由切削带形成过程的特征引起的，并随着切削深度的增加而增加，且主切削力Fz 的频率为115 Hz。研究结果表明：采用独立分量分析法进行噪声分离后更能精确识别切削力信号中的主要信息，减少噪声造成的误判。     

An approach based on current and sound signals for in-process tool wear monitoring

This paper presents a tool condition monitoring system (TCMS) for on-line tool wear monitoring in turning. The proposed TCMS was developed taking into account the necessary trade-off between cost and performance to be applicable in practice, in addition to a high success rate. The monitoring signals were the feed motor current and the sound signal. The former was used to estimate the feed cutting force using the least squares version of support vector machines (LS-SVM). Singular spectrum analysis (SSA) was used to extract information correlated with tool wear from the sound signal. The estimated feed cutting force and the SSA decomposition of the sound signal alone with the cutting conditions constitute the input data to the TCMS. Again LS-SVM was used to estimate tool condition and its reliability for on-line implementation was validated by experiments using AISI 1040 steel. The results showed that the proposed TCMS is fast and reliable for tool condition monitoring.     

不同熔滴过渡类型电弧声小波包频带能量特征提取

利用LabVIEW虚拟仪器设计了电弧声信号采集系统,并以MIG射滴过渡和射流过渡电弧声信号作为研究对象,采用小波包分解和重构电弧声信号,提取不同频带能量特征,构造识别射滴过渡和射流过渡的特征向量。研究表明:射滴过渡和射流过渡电弧声频谱主要集中在0~7 k Hz,射滴过渡电弧声能量在低频段(0~1.5 k Hz)有较高幅值,射流过渡在高频段(2~5 k Hz)有较高幅值,射滴过渡和射流过渡电弧声信号在S_(4,0)、S_(4,2)、S_(4,3)频带能量百分比差异明显,可作为识别射滴过渡和射流过渡的特征向量。     

Analysis of acoustic characteristics for plasma arc cutting

Abstract

The sound emitted during plasma arc cutting is closely related to the cutting conditions including cutting speed, arc current, operating gas flowrate, torch standoff height, nozzle shape, etc., and it therefore contains useful information for the evaluation of the plasma arc cutting process. The present work investigates the characteristics of the sound emitted during plasma arc cutting under various cutting conditions, using fast Fourier transformation and probability statistical analyses. An acoustic model of plasma arc cutting, having two jet sound sources, is proposed to interpret this sound. The sensitive frequency band of the cutting sound and the relationship between the cutting sound and the conditions are then revealed. It is shown that the cutting sound is a random signal readily affected by cutting conditions, and its energy is concentrated in the high frequency field and originates mainly from the mixing region of the first sound source and the mixing and transition regions of the second sound source. Experimental results also suggest the possibility of developing an acoustically based monitor system for this plasma arc process, and of reducing the acoustic exposure level, thereby improving working conditions.     

304不锈钢晶粒散射特性的超声检测分析

研究了304不锈钢不同热处理状态下晶粒尺寸对超声声速的影响。对同厚度钢和不锈钢的一次回波、二次回波和两者间的信号进行了频谱特性分析。结果表明,超声波在不锈钢中传播时,晶粒越大,声速越高;不锈钢的一次回波、二次回波及两者间的信号均在回波信号频带范围内,随晶粒度增加频率降低,散射作用增强。     

Using neural network for tool condition monitoring based on wavelet decomposition

This paper presents a neural network application for on-line tool condition monitoring in a turning operation. A wavelet technique was used to decompose dynamic cutting force signal into different frequency bands in time domain. Two features were extracted from the decomposed signal for each frequency band. The two extracted features were mean values and variances of the local maxima of the absolute value of the composed signal. In addition, coherence coefficient in low frequency band was also selected as a signal feature. After scaling, these features were fed to a back-propagation neural network for the diagnostic purposes. The effect on tool condition monitoring due to the presence of chip breaking was studied. The different numbers of training samples were used to train the neural network and the results were discussed. The experimental results show that the features extracted by wavelet technique had a low sensitivity to changes of the cutting conditions and the neural network has high diagnosis success rate in a wide range of cutting conditions.     

Characteristics analyzing and parametric modeling of the arc sound in CO2 GMAW for on-line quality monitoring

For on-line monitoring of welding quality, the characteristics of the arc sound signals in short circuit CO2 GMAW were analyzed in the time and frequency domains. The arc sound presents a series of ringing-like oscillations that occur at the end of short circuit i. e. the moment of arc re-ignition, and distributes mainly in the frequency band below 10 kHz. A concept of the arc tone channel and its equivalent electrical model were suggested, which is considered a time-dependent distributed parametric system of which the transmission properties depend upon the geometric and physical characteristics of the arc and surroundings, and is excited by the sound source results from the change of arc energy so that results in arc sound. The linear prediction coding （ LPC ） model is an estimation of the tone channel. The radial basis function （ RBF ） neural networks were built for on-line pattern recognition of the gas-lack in welding, in which the input vectors were formed with the LPC coefficients. The test results proved that the LPC model of arc sound and the RBF networks are feasible in on-line quality monitoring.