不同拉伸速度下的碳布/环氧树脂复合材料声发射评价

采用电子万能实验机控制,分别以1、2、5 mm/min的加载速度对碳布/环氧树脂复合材料进行拉伸,在拉伸过程中用声发射检测设备采集拉伸过程中产生的声发射信号,建立采集的声发射信号特征与时间、载荷的相关图,通过对相关图的分析,判断碳布/环氧树脂材料在拉伸过程中的损伤情况,并结合相关图分析不同拉伸速度对碳布/环氧树脂复合材料的影响,判定碳布/环氧树脂复合材料的临界失效载荷。结果表明:声发射检测可用于评价复合材料加载过程中的损伤情况,可将最大承载载荷的70%～80%作为碳布/环氧复合材料的失效参考载荷。     

风力机叶片主梁材料缺陷演化研究

本文以含分层缺陷的风力机叶片主梁复合材料为研究对象,采用单轴拉伸测试实验和声发射技术对分层缺陷演化进行研究。通过对分层缺陷复合材料在单轴拉伸过程中产生的声发射信号进行小波包分解,并对分解后的各层频谱和能量分布进行分析。研究结果表明:采用小波包时频、小波包能量谱联合分析,能够有效识别加载过程中的缺陷演化状态对应的声发射信号特征。得出了不同损伤模式即纤维断裂、基体开裂和层间断裂的频率分布范围。为分析主梁材料破损全过程的力学及声发射特性提供了一条新思路。     

C/SiC复合材料拉伸过程的声发射研究

利用声发射（AE）技术对C／SiC复合材料试样拉伸试验过程进行动态监测。通过声发射多参数分析法对拉伸过程中的声发射累计能量和平均持续时间随载荷或时间的变化进行了综合分析；同时对拉伸过程中典型AE信号的频率特征进行了分析，揭示了C／SiC复合材料拉伸损伤的演化过程及规律，给出了材料拉伸损伤发展的不同阶段以及各阶段损伤类型。通过声发射累计能量随载荷变化的斜率突变定义了材料临界损伤强度。     

蜂窝夹层复合材料的压缩损伤声发射特征

应用声发射技术对蜂窝夹层复合材料压缩损伤过程进行了试验研究。分析载荷与声发射信号关联图,依据其损伤过程和声发射特征,发现随着加载条件下载荷的增加,复合材料的损伤逐步增大。在加载初始阶段,仅有少量声发射信号,各种表征信号量小幅度增加;在加载中期,声发射信号增多,各种表征信号量不断增大;在加载后期,声发射信号有明显突增,各种表征信号量急剧增加。复合材料压缩损伤破坏与声发射的幅值、能量、撞击、上升时间、持续时间和计数等参量特征相关。根据各阶段特征参量滤波后所得信号分布与实际断裂位置相吻合。     

基于小波包特征熵的碳纤维复合材料损伤声发射信号特征提取方法

针对碳纤维复合材料层合板弯曲失效过程开展了声发射监测试验,并对试验采集的声发射信号进行了K-均值聚类分析,提取了不同损伤类型的声发射信号,对每种损伤类型的信号利用小波包特征熵的分析方法,选取能反映不同损伤类型的特征参数,实现对碳纤维复合材料不同损伤信号的有效识别,为碳纤维复合材料的损伤监测提供了理论依据。     

飞机复合材料拉伸过程损伤的声发射特性分析

利用声发射技术开展对拉伸载荷下飞机复合材料T型结构件的损伤发生过程研究,对复合材料试件拉伸载荷下损伤产生过程的机理进行了初步探讨。采用参数分析方法对拉伸试验采集到的声发射信号进行分析,得到该T型结构件拉伸过程中基体开裂、分层、纤维断裂至完全丧失承载能力时的声发射信号特征及其对应的载荷。结果表明,声发射技术能够准确预测复合材料T型结构件拉伸载荷下的损伤发生过程,可为该类结构件的检测提供参考。     

基于SVM的碳纤维/环氧复合材料声发射源识别

对单丝碳纤维/环氧树脂复合材料的试样以及碳布/环氧树脂试样拉伸,对其拉伸过程中产生的声发射源信号利用声发射波形参数作为不同神经网络的输入对其进行模式识别,发现SVM神经网络比BP神经网络在对声发射源信号进行模式识别时具有更高的预测准确率。在增加SVM网络训练集时,SVM网络的预测准确率有较大的提高。利用声发射波形参数与SVM对碳/环氧树脂复合材料进行模式识别得到较好的效果。     

声发射技术在三维编织复合材料测试中的应用研究

论述了声发射技术在三维编织复合材料拉伸过程损伤测试中的应用，结果表明，通过采集声发射参数可以描述复合材料在载荷情况下的内部变形的损伤机制。系统采用小波分析方法对声发射信号进行噪声处理，用频谱图描述复合材料的内部损伤变形特征，为复合材料的力学性能分析和材料复合工艺的改善提供理论基础。     

基于声发射技术的PDC复合片性能的分析

PDC钻头在钻进过程中出现崩齿、掉片以及聚晶金刚石(PDC)层脱落是影响钻头工作的关键因素。使用声发射仪采集了PDC复合片在加载过程中的声发射信号,并且采用小波分析方法对PDC的声发射信号进行了时频分解。结果表明,通过时频分解可以得到PDC复合片的AE特征信号;PDC复合片中弹性变形的信号频率集中出现在低频信号中,且AE信号的幅度与载荷大小呈正相关;高频信号中的突发型信号可以反映PDC层在载荷作用下的损伤情况。     

复合材料拉伸过程的声发射特性研究

为了研究16MnR／0Cr18Ni9Ti复合材料断裂过程的声发射特性,可以利用声发射技术对16MnR／OCr18Ni9Ti复合材料试件的拉伸过程进行全程监测。研究表明,材料拉伸断裂过程中,声发射信号丰富明显,可测性良好,并且不同破坏阶段的声发射信号具有不同的特征。通过对不同拉伸阶段声发射信号的参数分析,可以了解材料不同变形阶段的声发射特性,并据此来分析材料损伤的发生、发展及演变过程。与传统的力学试验方法相比,声发射技术在研究复合材料断裂过程方面具有明显的优势。     

Damage Characterization of Glass/Epoxy Composite Under Three-Point Bending Test Using Acoustic Emission Technique

Acoustic emission (AE) technique is an efficient non-destructive method for detection and identification of various damage mechanisms in composite materials. Discrimination of AE signals related to different damage modes is of great importance in the use of this technique. For this purpose, integration of k-means algorithm and genetic algorithm (GA) was used in this study to cluster AE events of glass/epoxy composite during three-point bending test. Performing clustering analysis, three clusters with separate frequency ranges were obtained, each one representing a distinct damage mechanism. Furthermore, time-frequency analysis of AE signals was performed based on wavelet packet transform (WPT). In order to find the dominant components associated with different damage mechanisms, the energy distribution criterion was used. The frequency ranges of the dominant components were then compared with k-means genetic algorithm (KGA) outputs. Finally, SEM observation was utilized to validate the results. The obtained results indicate good performance of the proposed methods in the damage characterization of composite materials.     

基于声发射信号模式的齿轮特征识别研究

鉴于声发射信号对齿轮早期裂纹具有独特的敏感性,对早期齿轮声发射信号的特征识别具有重要意义。介绍小波变换理论及其原理,建立齿轮疲劳试验平台,利用小波阈值降噪对不同工况下齿轮声发射信号进行预处理,获取高能量频段的信号并提取时域、频域特征参数,将其作为BP神经网络的输入,以识别不同工况下的声发射信号。实验结果表明：与去噪后的全频段信号相比,基于高能量频段信号所提取的特征参数具有更高的识别率,为早期齿轮故障信号分析和检测提供借鉴。     

Fuzzy pattern recognition of AE signals for grinding burn

Grinding burn is a common phenomenon of thermal damage that has been one of the main constraints in grinding difficult-to-machine materials. Grinding burn damages materials and degrades properties, by causing tensile residual stresses or microfractures in the workpiece surface. Numerous methods have been proposed to identify grinding burn. However, the main problems of current methods are their sensitivity and robustness. This paper describes a new method of grinding burn identification with highly sensitive acoustic emission (AE) techniques. The wavelet packet transform is used to extract features from AE signals and fuzzy pattern recognition is employed for optimising features and identifying the grinding status. Experimental results show that the accuracy of grinding burn recognition is satisfactory.     

钢丝绳声发射信号传播特性分析

针对钢丝绳断丝缺陷的在役动态监测需要,提出采用COMSOL软件对钢丝绳的声发射检测信号传播特性进行研究。首先建立钢丝绳结构模型,然后模拟不同频率、不同位置的断丝声发射信号在结构中的传播过程,获得不同位置的信号位移场及能量变化,最后根据导波理论,采用模态特征曲线结合小波变换分析不同类型声发射源的特征频率与模态成分。结果表明:声发射信号传播过程中,对于不同频率和激发方向的声发射源,其主要模态成分和特征频率的差异较大,可根据不同位置的时频分析结果,结合理论频散曲线判别声发射源信号的主要模态特征,并确定结构中声发射源的不同振动方向与中心频率。该研究结果可为钢丝绳损伤声发射检测提供理论参考依据。     

Discrete wavelet transform for tool breakage monitoring

Detection of tool breakage is of vital importance in automated manufacturing. Various methods have been attempted, and it is considered that the use of discrete wavelet transform (DWT), which is much more efficient and just as accurate wavelet analysis, may provide a realistic solution to the detection of tool breakage in operation. The DWT uses an analyzing wavelet function which is localized in both time and frequency to detect a small change in the input signals. In addition, it requires less computation than Fast Fourier Transformation (FFT). This paper discusses a tool breakage monitoring system based on DWT of an acoustic emission (AE) and an electric feed current signal using an effective algorithm. The experiment results show overall 98.5% reliability and the good real-time monitoring capability of the proposed methodology for detecting tool breakage during drilling.     

End milling tool breakage detection using lifting scheme and Mahalanobis distance

In this paper, a novel method based on lifting scheme and Mahalanobis distance (MD) is proposed for detection of tool breakage via acoustic emission (AE) signals generated in end milling process. The method consists of three stages. First, by investigating the specialty of AE signals, a biorthogonal wavelet with impact property is constructed using lifting scheme, and wavelet transform is carried out to separate AE components from the original signals. Second, Hilbert transform is adopted to demodulate signal envelope on wavelet coefficients and salient features indicating the tool state (i.e., normal conditions, slight breakage, and serious breakage) are extracted. Finally, tool conditions are identified directly through the recognition of these features by means of MD. Practical application results on a CNC vertical milling machine tool show that the proposed method is accurate for feature extraction and efficient for condition monitoring of cutting tools in end milling process.     

基于小波包能量谱的磁记忆检测方法

提出磁记忆信号的小波包能量谱分析方法,以小波包变换理论为基础,通过对有中心小孔缺陷的45号钢和18CrNi4A试样进行拉伸试验,采集不同载荷下试样表面的磁记忆信号。用sym6小波对所得信号进行小波包能量谱特征分析。该方法可有效判断试样的应力集中部位及应力集中程度。