结构声发射的数值模拟及实验研究

由于声发射源产生的瞬态弹性波会在传播过程中产生各种频率和模态的声发射信号,这给应用声发射技术进行无损检测带来一定困难。针对声发射信号在实际介质中传播的复杂性,为了研究弹性波的传播对波形的影响,提出了利用有限元方法模拟声发射信号在薄铝板的传播及几何形状边界反射过程,得到声发射波形的时域特征,进一步分析了声发射信号在铝板中的传播特性;搭建了与有限元模型相应的声发射断铅实验系统来采集断铅信号。试验及研究结果表明,利用声发射断铅实验系统获得的信号与有限元计算方法得到的模拟信号基本吻合,说明有限元数值计算方法可以作为分析声发射信号传播过程的一种手段;该结果为基于声发射的结构缺陷检测技术提供了数值模型,同时声发射技术用于无损检测也有了更精确的理论基础。     

动车组轴箱轴承状态的声发射检测机理研究

声发射技术因具有高频、高灵敏度的感知能力,可应用于动车组轴箱轴承的状态监测和故障诊断,但目前尚缺乏轴承状态与声发射信号之间的机理性研究.以动车组轴箱轴承为研究对象,应用赫兹接触理论,基于微凸体随机分布假设,建立了轴承粗糙表面的接触模型;采用弹性力学理论,进行了轴承受载条件下的微凸体弹性变形分析;结合弹流理论,进而建立了润滑条件下滚动体与内、外圈之间的粗糙接触表面变形能量方程;分析轴承接触状态声发射信号的弹性波表征特点,获取轴承变形能与声发射感知的RMS之间的变化关系,从而完成了轴承的声发射检测机理研究.以典型双列圆锥滚子轴承的外圈故障为研究实例,通过理论分析和试验结果之间的比对,验证了机理分析的正确性,为声发射感知在轴承状态检测的应用提供了理论支持.     

运用声发射技术监测金属塑性成型过程中润滑状态的研究

为监测金属塑性成型过程中的润滑状态,采用运用声发射技术,通过对无润滑和有润滑时金属塑性变形过程和摩擦过程的监测及对比,分别研究了金属摩擦声发射信号和塑性变形声发射信号.结果表明,不同材料摩擦产生的声发射信号数值上大小不同,同种材料摩擦声发射信号数值上小于塑性变形声发射信号;采用声发射技术,基于实时波形和声发射信号参数的平均值都能监测金属塑性成型时的润滑状态.     

车削加工中的声发射的研究

<正> 声发射(简称AE)是受力物体由不稳定的高能状态向低能状态过渡时释放出来的弹性波,它是由材料内部缺陷、裂纹或微观不均匀所引起的,目前已在无损检测方面得到较广泛的应用。从本世纪70年代开始,日、美、英等国都相继开展了应用声发射监控刀具破损和磨损裂纹的研究工作,取得了一定的进展。本文试图用试验方法探求声发射信号与切削过程之间的关系,寻求适宜的衡量刀具磨损量的指标,并考察用声发射进行切屑形状自动识别的可能性。     

金属材料类别对动态摩擦声发射参数影响的实验研究

利用声发射技术研究了材料类别对动态摩擦状态的影响.结果表明:材料类别对摩擦产生的声发射信号有很大影响,动态摩擦产生的声发射信号的强弱与材料之间的硬度差有关,硬度差越大,摩擦产生的声发射信号的能量差别越大;声发射信号的能量可以反映摩擦学中的"犁沟效应"的差别.     

管道声发射信号衰减特性随管道壁厚变化研究

为了建立压力管道泄漏声发射衰减定位模型,需要开展压力管道泄漏声发射试验研究。由于试验管道无法按1∶1比例采用核电站压力管道原型,有必要针对相同材质和加工工艺、不同壁厚和外径的金属管道,获取声发射信号在不同壁厚的金属管道中的传播特性规律及差异,揭示金属管道壁厚对声发射波传播衰减的影响。分别采用3种不同尺寸规格的金属管道作为试验对象,运用声发射探测技术获得声发射信号沿着不同壁厚的金属管壁传播的衰减规律,揭示了金属管道壁厚对声发射波传播的影响。结果表明,管道声发射信号的衰减特性随着管道壁厚的不同而变化,在薄壁管中声发射波在传播路径中会产生模式转换,即发生频散现象,各种模式的波相互叠加使信号幅度沿着传播路径未呈单调衰减趋势,用声发射信号的衰减特性做定位时须考虑这一点。     

基于小波尺度谱的转子系统碰摩声发射特性

针对转子系统动静件间发生碰摩时会引起弹性应变而产生声发射,进而可利用声发射来辨识和诊断碰摩故障的特点,首先对碰摩声发射和碰摩振动信号进行了试验对照研究,讨论基于声发射的碰摩辨识别方法的独特优越性.然后,着重对碰摩声发射的特性进行了试验研究,并借助于小波尺度谱优越的时频分析性能,利用小波尺度谱对碰摩声发射的时频特性、传播特性和频散特性等进行了详细分析.分析结果显示小波尺度谱非常适合碰摩声发射这种频率丰富、非平稳和非线性的多模态波,是模态声发射分析的有力方法.     

声发射技术及其在压力容器检测中的应用

声发射技术是一种新的材料评价方法。所谓声发射,就是贮存在构件内部的能量释放时所产生的弹性应力波。换句话说,当物体受外力或内应力作用时,缺陷处因应力集中而产生塑性形变,其贮存能量的一部分以应力波的形式释放出来,这种现象就叫做声发射。利用电子技术接收发射出来的应力波并加以处理,以评价缺陷发生、发展的规律和寻找缺陷位置的技术,称为声发射技术。 声发射技术和以往评价材料的方法如超声、磁粉、涡流、射线、渗透等有着本质的差别:在以往的方法中,承载构件或材料所存在的裂纹、缺陷,是在静态下被发现的;而在声发射检测技术中,裂纹、缺陷则处于动态下,它“主动地”参与检测,即在压力的作用下缺陷本身发出信号──应力波。检验者根据缺陷发射应力波的特点和诱发应力波的外界条件,不仅可以了解缺陷的目前状态,还可了解缺陷的形成历史和它在今后使用条件下发展和增大的趋势,据以估计缺陷的危险性。这是以往其他的材料评价方法所难以做到的。     

基于声发射的Q235钢点蚀过程监测技术研究

以声发射技术为基础,针对油气储运常用材料Q235的点蚀过程,利用试件在模拟溶液中点蚀过程产生的声发射信号为样本,采用参量分析与波形分析相结合,重点研究Q235钢点蚀过程中产生的声发射信号的变化特征,得到了金属点蚀不同阶段的声发射信号特性,并通过声发射信号事件的分布规律,得到了Q235钢早期腐蚀与声发射信号演变的一致性结果。试验证明了声发射技术为基础的Q235钢点蚀过程监测具有良好效果。     

声发射技术在斜切入磨削控制中的应用试验

声发射(Acoustic Emission)是固体材料在塑性变形及开裂过程中以弹性波的形式释放应变能的一种物理现象。这种现象已成功地在无损检测中用以检测构件或材料内部的缺陷。在磨削加工中,当砂轮与工件接触时,由于砂粒与工件的摩擦和金属的塑性变形与剥离也产生强烈的高频声发射信号,其频率在50kHz到1MHz之间。信号的时域和频域结构与砂轮和工件的接触状况、砂轮表面形貌和磨削过程有关。利用声发射信号进行磨削过程控制和机理研究具有灵敏度高、不受低频振动干扰的优点。通过磨削过程的声发射信号研究,揭示其相关性,可以为磨削过程控制提供一种新的途径。     

Determination of the damage mechanisms in armor structural materials via self-organizing map analysis

Dynamic compression fracture behaviors together with damage mechanisms of materials subjected to a compressive load at a high strain rate were studied by using a Self-organizing map (SOM). The materials used for the analysis were Al5083, Rolled homogeneous armor (RHA) and tungsten heavy alloy (WHA). The deformation behavior and Acoustic emission (AE) signal were acquired through a Split Hopkinson pressure bar (SHPB)-AE coupled test. The self-organization map which is one of the artificial neural network technique was employed to classify the AE energy, amplitude, and peak frequency according to the characteristics of the signal. In addition, distributions of AE signals were represented in stress-strain curves. The correlation between AE characteristics and material damage mechanisms was investigated. Based on the results, it was found that cluster 1 with high AE energy, high amplitude and low frequency was the cluster of the AE signal generated near the yield point of the material. Cluster 3, which has the opposite tendency, was confirmed as a cluster of AE signals that occurred just before a fracture of the material. The change in the measured value can be seen depending on the strain rate and the material, but the overall tendency was similar.     

基于虚拟仪器的声发射信号处理平台

虚拟仪器代表着目前测试仪器领域的发展方向,LabVIEW语言是一种功能强大的仪器开发平台。声发射检测是一种动态无损检测方法,即在构件或材料的内部结构缺陷或潜在缺陷处于运动变化的过程中进行无损检测。本文设计了基于LabVIEW的声发射信号处理平台,硬件上采用美国PAC公司的PCI-2数据采集卡,满足对声发射信号的采集要求,软件平台可实现对声发射信号的小波分析、频谱分析、参数分析以及报警等功能。     

轮轴弯曲刚度对轮轨垂向动态载荷影响分析

以国内某型地铁车辆为例,研究轮轴弯曲刚度对轮轨垂向动态载荷和轮对垂向振动的影响。在常规多刚体动力学模型的基础上,结合BM3000轮对和北京地铁轮对两种不同的弹性轮对模型,对比分别采用刚性轮对模型和弹性轮对模型时的轮对垂向振动加速度和轮轨垂向力。结果表明,对BM3000弹性轮对模型来说,由于其弯曲刚度相对较小,随着运行速度的增大,轮对垂向振动加速度和轮轨力与刚性轮对的差距不断加大,而对于轮轴弯曲刚度较大的北京地铁轮对来说,其弹性轮对模型和刚性轮对模型的结果比较接近,在计算的速度下轮对的振动峰值及频率均有明显的降低。因而,通过加大轮轴弯曲刚度可明显改善轮对的垂向振动和轮轨垂向力,实现改善轮轨动态接触状态的目的。     

Inspection of locomotive bearing rings by the acoustic-emission method

The results of testing the bearing rings (BRs) of a locomotive axle box by the acoustic-emission (AE) method are presented. The small size of the localization zone of BRs leads to a significant spread of the coordinates of flaws even at minimal errors of the time of arrival of AE signals at piezoelectric-antenna probes. To refine the testing technique, the maximum testing load necessary for initializing AE signals is determined. Deformations in BRs are measured with a strain-gauging system related to the AE system. During recording of AE signals, the mechanical stresses are measured, thus allowing rejection of parasitic signals and an increase in the localization accuracy.     

Vibration impact acoustic emission technique for identification and analysis of defects in carbon steel tubes: Part B Cluster analysis

Current steel tubes inspection techniques are invasive, and the interpretation and evaluation of inspection results are manually done by skilled personnel. Part A of this work details the methodology involved in the newly developed non-invasive, non-destructive tube inspection technique based on the integration of vibration impact (VI) and acoustic emission (AE) systems known as the vibration impact acoustic emission (VIAE) technique. AE signals have been introduced into a series of ASTM A179 seamless steel tubes using the impact hammer. Specifically, a good steel tube as the reference tube and four steel tubes with through-hole artificial defect at different locations were used in this study. The AEs propagation was captured using a high frequency sensor of AE systems. The present study explores the cluster analysis approach based on autoregressive (AR) coefficients to automatically interpret the AE signals. The results from the cluster analysis were graphically illustrated using a dendrogram that demonstrated the arrangement of the natural clusters of AE signals. The AR algorithm appears to be the more effective method in classifying the AE signals into natural groups. This approach has successfully classified AE signals for quick and confident interpretation of defects in carbon steel tubes.     

Dependence of the number of acoustic-emission pulses on the mechanical testing load of bearing races in an axle unit

Acoustic-emission tests of flawless bearing races in axle units are performed on a special loading stand. Dependences of the number of AE pulses on the loading force are established for objects with and without flaws. The spatial distribution of AE sources in the flawless bearing races and a change in activity during reloading of the races are studied. Numerical parameters of the local-dynamic rejection criterion are determined on the basis of the analysis of the experimental data on races containing fatigue cracks.     

Reconstructing the Amplitudes of Radiation of a Defect Based on Acoustic Emission Signals at the Free Boundary of a Massive Body

We consider the problem in the dynamic theory of elasticity about steady-state oscillations in a massive elastic body that is at the pre-fracture stage of material under antiplane strain. The process of acoustic emission (AE) at the stage of accumulation of defects representing a finite tree graph directed toward the free boundary of the elastic body is investigated. The problem about the radiation of a defect has been solved under the assumption of the activity of its part nearest to the body boundary. A variational approach was used to solve the inverse problem of reconstructing the amplitudes of radiation of the defect based on AE signals.

     

Feature extraction of AE characteristics in offshore structure model using Hilbert-Huang transform

This paper addresses an application of recently developed signal processing tool based on the Hilbert-Huang transform (HHT) to characterize the acoustic emission (AE) signals released from the offshore structure model. The AE signals from the cracks in the welded steel nodes of the offshore structure model are collected during the tensile testing in water and the simulated AE signals are also collected in the offshore structure model. Instantaneous frequency and energy features based on local properties of the AE signals are then extracted using the Hilbert-Huang transform method. In order to demonstrate the advantage of the AE feature extraction techniques based on Hilbert-Huang transform, the conventional AE analysis is also provided side-by-side for comparison. The results verify that the method based on HHT better characterizes the AE signals than the classical AE techniques. It can be concluded that the AE signal analysis based on HHT is an effective tool to extract the features and this opens perspectives for crack recognition in offshore structures.     

基于声发射信号的外圆切入磨削去除率监测

针对精密外圆切入磨削加工的在线监测需求,提出一种采用声发射信号实现轴类零件材料去除率在线监测的方法。根据声发射信号强度与磨削力之间的联系,建立了声发射信号均方根曲线的预测模型,利用该预测模型研究了砂轮进给阶段和驻留阶段磨削系统时间常数的理论计算方法,推导了声发射信号均方根曲线与工件材料去除率的关系;编写了在线监测软件,利用声发射传感器实现了精密外圆切入磨削的材料去除率预测。实验证明,所建立的声发射信号均方根曲线模型具有良好的预测精度,基于该模型能够实现磨削系统时间常数在线评估,并实现精密轴类零件材料去除率的实时在线监测。     

The Application of an ANFIS and Grey System Method in Turning Tool-Failure Detection

The cutting process is a major material removal process; hence, it is important to search for ways of detecting tool failure. This paper describes the results of the application of an adaptive-network-based fuzzy inference system (ANFIS) for tool-failure detection in a single-point turning operation. In a turning operation, wear and failure of the tool are usually monitored by measuring cutting force, load current, vibration, acoustic emission (AE) and temperature. The AE signal and cutting force signal provide useful information concerning the tool-failure condition. Therefore, five input parameters of the combined signals (AE signal and cutting force signal) have been used in the ANFIS model to detect the tool state. In this model, we adopted three different types of membership function for analysis for ANFIS training and compared their differences regarding the accuracy rate of the tool-state detection. The result obtained for the successful classification of tool state with respect to only two classes (normal or failure) is very good. The results also indicate that a triangular MF and a generalised bell MF have a better rate of detection. We also applied grey relational analysis to determine the order of influence of the five cutting parameters on tool-state detection.