Damage classification in carbon fibre composites using acoustic emission: A comparison of three techniques

Classifying the type of damage occurring within a structure using a structural health monitoring system can allow the end user to assess what kind of repairs, if any, that a component requires. This paper investigates the use of acoustic emission (AE) to locate and classify the type of damage occurring in a composite, carbon fibre panel during buckling. The damage was first located using a bespoke location algorithm developed at Cardiff University, called delta-T mapping. Signals identified as coming from the regions of damage were then analysed using three AE classification techniques; Artificial Neural Network (ANN) analysis, Unsupervised Waveform Clustering (UWC) and corrected Measured Amplitude Ratio (MAR). A comparison of results yielded by these techniques shows a strong agreement regarding the nature of the damage present in the panel, with the signals assigned to two different damage mechanisms, believed to be delamination and matrix cracking. Ultrasonic C-scan images and a digital image correlation (DIC) analysis of the buckled panel were used as validation. MAR’s ability to reveal the orientation of recorded signals greatly assisted the identification of the delamination region, however, ANN and UWC have the ability to group signals into several different classes, which would prove useful in instances where several damage mechanisms were generated. Combining each technique’s individual merits in a multi-technique analysis dramatically improved the reliability of the AE investigation and it is thought that this cross-correlation between techniques will also be the key to developing a reliable SHM system.     

基于聚类分析的罐底声发射检测信号融合方法

采用声发射技术进行罐底腐蚀与泄漏检测过程中,需要对多个传感器的检测到的声发射信号进行融合处理,将属于同一声发射源的声发射信号判定为一个声发射事件。但是在现场检测过程中,由于噪声的存在,使得在声发射信号融合处理时容易对声发射源产生误判。针对该问题,提出了一种基于聚类分析的罐底声发射信号融合方法,其基本原理是先根据事件定义时间进行初始声发射事件判定,然后采用聚类分析方法对初始声发射事件中的信号进行分类,将每一类信号分别判定为一个声发射源。现场实验表明采用该方法抗噪声干扰能力强、误判概率低,能准确反应罐底腐蚀的实际情况。     

基于EMD的胶合板损伤声发射信号特征提取及神经网络模式识别

摘要：针对胶合板损伤声发射信号的非平稳性和损伤类别特征相互重叠的实际情况,提出了基于经验模态分解(Empirical Mode Decomposition, EMD)和BP神经网络相结合的信号特征提取和识别方法。首先对损伤声发射信号进行EMD分解,筛选出包含主要信息的本征模态函数（Intrinsic Mode Function, IMF）分量;其次构建以各IMF分量的能量占比作为表征各损伤信号的特征向量;最后以提取的特征向量为输入样本,建立BP神经网络模式分类器对四类胶合板损伤信号进行识别。五层胶合板损伤的实测数据表明,该方法能够准确地提取出声发射信号特征并对其损伤类型进行有效地识别。     

Acoustic emission detection of fatigue cracks in wind turbine blades based on blind deconvolution separation

The occurrence and expansion of fatigue cracks in large wind turbine blades may lead to catastrophic blade failure. Each fatigue phase of a material has been associated with a typical set of acoustic emission (AE) signal frequency components, providing a logical base for establishing a clear connection between AE signals and the fatigue condition of a material. The relevance of efforts to relate recorded AE signals to a material's mechanical behaviour relies heavily on accurate AE signal processing. The main objective of the present study is to establish a direct correlation between the fatigue condition of a material and recorded AE signals. We introduce the blind deconvolution separation (BDS) approach because the result of AE monitoring is usually a convoluted mixture of signals from multiple sources. The method is implemented on data acquired from a fatigue test rig employing a wind turbine blade with an artificial transverse crack seeded in the surface at the base of the blade. Two different sets of fatigue loading were conducted. The convoluted signals are collected from the AE acquisition system, and the weak crack feature is extracted and analysed based on the BDS algorithm. The study reveals that the application of BDS‐based AE signal analysis is an appropriate approach for distinguishing and interpreting the different fatigue damage states of a wind turbine blade. The novel methodology proposed for fatigue crack identification will allow for improved predictive maintenance strategies for the glass‐epoxy blades of wind turbines. The experimental results clearly demonstrate that the AE signals generated by a fatigue crack on a wind turbine blade can be synchronously separated and identified. Characterizing and assessing fatigue conditions by AE monitoring based on BDS can prevent catastrophic failure and the development of secondary defects, as well as reduce unscheduled downtime and costs. The possibility of using AE monitoring to assess the fatigue condition of fibre composite blades is also considered.     

Damage Classification of Sandwich Composites Using Acoustic Emission Technique and k-means Genetic Algorithm

In this study acoustic emission (AE) technique was used for monitoring mode I delamination test of sandwich composites. Since, during mode I delamination test various damage mechanisms appear, their classification is of major importance. Hence, integration of \(k\) -means algorithm and genetic algorithm was applied as an efficient clustering method to discriminate different failure modes. Performing primary experiments to find the relationship between AE parameters and damage mechanisms, the AE signals of obtained clusters were assigned to distinct damage mechanisms. Also, the dominance of damage mechanisms was determined based on the distribution of AE signals in different clusters. Finally SEM observation was employed to verify obtained results. The results indicate the efficiency of the proposed method in damage classification of sandwich composites.     

Real-time damage mechanisms assessment in CFRP samples via acoustic emission Lamb wave modal analysis

This paper proposes a very promising acquisition-analysis procedure to evaluate real-time damage in carbon fiber-reinforced polymer (CFRP) composite plates by means of the acoustic emission (AE) method. It shows how, by using appropriate acquisition frequency filters and very narrow time windows, it is possible to avoid reflection at boundaries and successfully split the A0 and S0 Lamb modes of the AE signals. After that, an appropriate algorithm —based on the comparison of strength of both modes in time and frequency domains— allows one to associate each AE event to a particular damage mechanism (delamination, fiber breaking and matrix micro-cracking). Experimental results from three point bending tests carried out on 22-layer CFRP samples, with delamination artificially induced by a Teflon film, clearly demonstrate the real-time evaluation of the induced delamination and the beginning and growth of new ones.     

Damage Modes Recognition and Hilbert-Huang Transform Analyses of CFRP Laminates Utilizing Acoustic Emission Technique

Discrimination of acoustic emission (AE) signals related to different damage modes is of great importance in carbon fiber-reinforced plastic (CFRP) composite materials. To gain a deeper understanding of the initiation, growth and evolution of the different types of damage, four types of specimens for different lay-ups and orientations and three types of specimens for interlaminar toughness tests are subjected to tensile test along with acoustic emission monitoring. AE signals have been collected and post-processed, the statistical results show that the peak frequency of AE signal can distinguish various damage modes effectively. After a AE signal were decomposed by Empirical Mode Decomposition (EMD) method, it may separate and extract all damage modes included in this AE signal apart from damage mode corresponding to the peak frequency. Hilbert-Huang Transform (HHT) of AE signals can clearly illustrate the frequency distribution of Intrinsic Mode Functions (IMF) components in time-scale in different damage stages, and can calculate accurate instantaneous frequency for damage modes recognition to help understanding the damage process.     

ESEM investigations for assessment of damage kinetics of short glass fibre reinforced thermoplastics - Results of in situ tensile tests coupled with acoustic emission analysis

The damage mechanisms of short glass fibre reinforced polypropylene (PP) and polybutene-1 (PB-1) materials were investigated. For this purpose, in situ tensile tests were conducted in the environmental scanning electron microscope (ESEM) while simultaneously recording the acoustic emission (AE). To be able to observe damage mechanisms directly during loading, notched specimens were used. This method allows the direct correlation of the recorded load - elongation data with observed damage mechanisms, as well as correlations with acoustic emission data. Hence, it is possible to describe the damage kinetics of short glass fibre composite.It was found that different bonding conditions of the two investigated materials result in different damage mechanisms as well as in different AE behaviour. For fibre reinforced PP with excellent bonding conditions of the fibres in the polymeric matrix, fibre fracture, slipping of fibres in the delamination area, debonding and pull-out with matrix yielding was observed. The determined AE parameter amplitude A_p and energy E_AE for the PB-1 material are lower because of the weak bonding of the fibres to the PB-1-matrix. Hence, energy dissipative damage mechanisms like pull-out with matrix yielding can occur only in a limited part of such materials.     

Characterization of acoustic emission signals from particulate filled thermoset and thermoplastic polymeric coatings in four point bend tests

Four point bend tests were conducted on two previously characterized particulate filled thermoplastic and thermoset polymeric coatings, the acoustic emission (AE) method being utilized to monitor the damage progress during the tests. Different damage mechanisms operating in the polymeric coatings can be recognized by the different amplitude range of the AE signals emitted. AE can be used to assess the relative strength of particle/matrix bonds in particulate filled polymeric coatings.     

Detection of fibre fracture and ply fragmentation in thin-ply UD carbon/glass hybrid laminates using acoustic emission

This paper investigates the link between acoustic emission (AE) events and the corresponding damage modes in thin-ply UD carbon/glass hybrid laminates under tensile loading. A novel configuration was investigated which has not previously been studied by AE, where the laminates were fabricated by embedding thin carbon plies between standard thickness translucent glass plies to produce progressive fragmentation of the carbon layer and delamination of the carbon/glass interface. A criterion based on amplitude and energy of the AE event values was established to identify the fragmentation failure mode. Since the glass layer was translucent, it was possible to quantitatively correlate the observed fragmentation during the tests and the AE events with high amplitude and energy values. This new method can be used as a simple and advanced tool to identify fibre fracture as well as estimate the number and sequence of damage events that are not visible e.g. in hybrid laminates with thick or non-transparent layers as well as when the damage is too small to be visually detected.     

Coal Rock Condition Detection Model Using Acoustic Emission and Light Gradient Boosting Machine

Coal rock mass instability fracture may result in serious hazards to underground coal mining. Acoustic emissions (AE) stimulated by internal structure fracture should carry lots of favorable information about health condition of rock mass. AE as a sensitive non-destructive test method is gradually utilized to detect anomaly conditions of coal rock. This paper proposes an improved multi-resolution feature to extract AE waveform at different frequency resolutions using Coilflet Wavelet Transform method (CWT). It is further adopt an efficient Light Gradient Boosting Machine (LightGBM) by several cascaded sub weak classifier models to merge AE features at different views of frequency for coal rock anomaly damage recognition. The results denote that the proposed method achieves excellent recognition performance on anomaly damage levels of coal rock. It is an effective method to detect the critical stability further to predict the rock mass bursting in time.     

Principal Component Analysis of Acoustic Emission Signals From Landing Gear Components: An Aid to Fatigue Fracture Detection

Abstract: This work forms part of a larger investigation into fatigue crack detection using acoustic emission (AE) during landing gear airworthiness testing. It focuses on the use of principal component analysis (PCA) to differentiate between fatigue crack propagation (FCP) signals and high levels of background noise. An artificial AE fracture source was developed and additionally five sources were used to generate differing artificial AE signals. Signals were recorded from all six artificial sources in a real landing gear component subject to no load. Furthermore, artificial FCP signals were recorded in the same component under airworthiness test load conditions. PCA was used to automatically differentiate between AE signals from different source types. Furthermore, successful separation of artificial FCP signals from a very high level of background noise was achieved. The presence of a load was observed to affect the ultrasonic propagation of AE signals.     

Health monitoring of carbon/carbon,woven reinforced composites. Damage assessment by using advanced signal processing techniques. Part I: Acoustic emission monitoring and damage mechanisms evolution

The present work, first of two parts, deals with three types of woven carbon/carbon (C/C) composites having differentiations during the manufacturing procedure, which influences their fibre/matrix interface. All material types were tested under tensile loading in a load–unload–reload configuration, with online acoustic emission monitoring. Unsupervised pattern recognition algorithms were utilized to classify the acoustic emission (AE) data recorded during the tests. The resulted clusters, concluded by the analysis of AE hits, are associated with the damage mechanisms of the material, activated at the different load levels, and significant remarks were extracted regarding the damage evolution and its differentiation according to the different fibre/matrix interfaces. Emphasis is given on the impact of the different interface types upon the total mechanical behavior and damage accumulation at the test coupons. A qualitative evaluation of the interfaces using non-destructive testing data is also attempted. This first part intends to propose methodologies and procedures to analyze data from online acoustic emission monitoring in order to extract useful information regarding the damage evolution within C/C materials.     

Optimization of Positioning of an Acoustic Emission Sensor for Monitoring Hot Forging

Optimization of a sensor location for effective characterization of a hot forging process using acoustic emission (AE) signals is discussed in this paper. Acoustic emission signals generated during forging operations on an aluminium alloy were recorded using three sensors simultaneously by mounting them on the top bolster, bottom bolster, and bottom die of the press. The AE signals with maximum sensitivity could be detected with a sensor attached to the bottom die in preference to the other positions. Using AE parameters, the forging process could be differentiated into three regions, i.e., 1) yielding of the workpiece material, 2) intermediate deformation region, and 3) filling of the die. The results show that the optimum position of the AE sensor for monitoring hot forging is found to be the bottom die of the forging press.     

Acoustic emission analysis for tool wear monitoring in face milling

Monitoring the condition of the cutting tool in any machining operation is very important since it will affect the workpiece quality and an unexpected tool failure may damage the tool, workpiece and sometimes the machine tool itself. Advanced manufacturing demands an optimal machining process. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the most promising tool monitoring techniques is based on the analysis of Acoustic Emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process. Various approaches have been taken to monitor progressive tool wear, tool breakage, failure and chip segmentation while supervising these AE signals. In this paper, AE analysis is applied for tool wear monitoring in face milling operations. Experiments have been conducted on En-8 steel using uncoated carbide inserts in the cutter. The studies have been carried out with one, two and three inserts in the cutter under given cutting conditions. The AE signal analysis was carried out by considering signal parameters such as ring down count and RMS voltage. The results show that AE can be effectively used to monitor tool wear in face milling operation.     

Damage monitoring and identification in SiC/SiC minicomposites using combined acousto-ultrasonics and acoustic emission

The acoustic emission (AE)-based technique is considered to be a promising way to real-time monitoring of microstructural changes and damage evolution in Ceramic Matrix Composites (CMCs). The present paper proposes a testing protocol that combines acousto-ultrasonics (AU) and acoustic emission (AE) monitoring, with a view to obtain both global and local definite characteristics on damage modes and kinetics. It is developed and assessed on SiC/SiC minicomposites, which are appropriate test specimens to establish sound relations between mechanical behavior and damage modes. AU wave velocity measurements provide a global measure of matrix cracking damage and the relations between crack growth and damage characteristics. AE monitoring allows accurate localization of AE sources taking into account wave velocity dependence to damage as well as differentiation of the damage modes, which control the mechanical behavior. Finally, multivariate analysis of AE data allowed classification of signals into clusters, which were successfully associated to the various damage modes.     

Acoustic and Electromagnetic Emissions as Precursor Phenomena in Failure Processes

Abstract: In this work, we measured the electromagnetic field, given by the moving charges, during laboratory fracture experiments on specimens made of different heterogeneous materials. We investigated the mechanical behaviour of concrete and rocks samples loaded up to their failure by the analysis of acoustic emission (AE) and electromagnetic emission (EME). All specimens were tested in compression at a constant displacement rate and monitored by piezoelectric (PZT) transducers for AE data acquisition. Simultaneous investigation into magnetic activity was performed by a measuring device calibrated according to metrological requirements. In all the considered cases, the presence of AE signals has been always observed during the damage process, whereas it is very interesting to note that the magnetic signals were generally observed only in correspondence to sharp stress drops or the final collapse.     

Determination of the damage threshold in woven-ply thermoplastic laminates at T > Tg: Acoustic emission and microscopic damage analysis

An original in situ measurement of acoustic emission (AE) was applied to monitor damage progress in discrete steps during gradual load/unload tensile tests on [±45°]₇ C/PPS laminates at temperatures T > Tg, when matrix ductility is enhanced. In order to understand the specific damage behavior of such materials under severe environmental conditions, AE analysis was accompanied by microscopic observations to detect the damage initiation threshold as well as the damage mechanisms within the composite material. Once the AE source mechanisms have been separated into classes thanks to the pattern recognition software Noesis, they have been identified to match physical phenomena. Earliest cracks events occur at the crimps where the rotation of warp/weft fibres takes place, followed by the intra-bundles splitting on free surface. It is observed that the onset of intralaminar cracking and debonding is affected by the presence of matrix-rich regions between the plies, because of an extensive plasticization of the PPS matrix. The study of the specific acoustic activity of neat PPS resin specimens confirms that the local plastic deformation in matrix-rich areas contributes to delay the initiation of damage, and subsequent AE signals. Finally, AE proved to be a relevant technique to investigate damage mechanisms and to determine accurately the damage threshold in TP-based composites to be used in aeronautical applications at T > Tg.     

Optimal design and testing of laminated light-weight composite structures with local reinforcements considering strength constraints: Part II: Testing

The present paper explains details of the measurement methods applied in the testing of strength-optimized locally reinforced laminates whose design has been explained in Part I. The measurement methods are an optical surface method, based on Digital Image Correlation (DIC), and measurements of Acoustic Emission (AE) which can originate from anywhere within the test specimen. Both methods are intended to identify early damage events or accumulation and the corresponding loads are compared to predicted first-ply-failure loads. The DIC method identifies non-linearities in the displacement path which infer that damage events have occurred. The AE method utilizes the signal energy rate per time to identify damage events. The results of the particular specimens show no correlation between the two measurement methods. The averaged values for the different type specimens show a significant dependency.     

Monitoring mechanical damage in structural materials using complimentary NDE techniques based on thermography and acoustic emission

This work deals with nondestructive evaluation (NDE) of the fracture behavior of metallic materials by combining thermographic and acoustic emission (AE) characterization. A new procedure, based on lock-in infrared (IR) thermography, was developed to determine the crack growth rate using thermographic mapping of the material undergoing fatigue. The thermography results on crack growth rate were found to be in agreement with measurements obtained by the conventional compliance method. Furthermore, acoustic emission was used to record different cracking events. The rate of incoming signals, as well as qualitative features based on the waveform shape, was correlated with macroscopically measured mechanical parameters, such as load and crack propagation rate. Additionally, since the failure modes have distinct AE signatures, the dominant active fracture mode was identified in real time. The application of combined NDE techniques is discussed for characterizing the damage process which leads to catastrophic failure of the material, thereby enabling life prediction in both monolithic aluminum alloys and aluminum alloy/SiC particle (SiC_p) reinforced composites.