AE Signals Research Articles

Buckling assessment of GFRP and carbon fiber-reinforced plastic filament-wound tubes using an acoustic emission-based methodology

The aim of this research is to investigate the failure mechanisms of the filament-wound composite tubes under axial compressional loading by using an acoustic emission approach. First, the mechanical properties of ±45°C composite tubes were obtained experimentally. Then, failure due to the buckling phenomenon and crashworthiness characteristics were studied utilizing numerical simulation and experimental methods. Tubes were next simulated in ABAQUS software, and a continuum damage mechanics model was implemented in a progressive framework to assess the failure modes. From the macroscale view, results showed that the damage behavior of composite tubes turned out to be dominated by local buckling followed by a post-buckling field, which is generated by longitudinal cracks along the winding direction. On the micro-scale, the acoustic emission-based procedure based on the wavelet packet transform method was adopted. The hierarchical modeled assessment resulted in the identity of four clusters of AE signals. In GFRP tubes, the fiber breakage and fiber/matrix separation could mostly control the higher percentage of damage and cause to increase the energy absorption. Finally, by comparing the results obtained from micro and macro scales, the local buckling failure mode was attributed to the low content of fiber/matrix debonding in the structure.

Revealing thermal-mechanical hybrid ablation mechanism in carbon fiber reinforced plastic laser cutting by acoustic emission monitoring

The ablation mechanism of CFRP laser cutting refers to complex thermal and mechanical behaviour, and should be investigated. Two processing mechanisms of thermal ablation (TA) and mechanical denudation (MD) were found. TA resulted from the vaporization of the material formed by the accumulation of laser energy, which is reflected by RMS of AE signal < 0.14 V. MD resulted from the material that was stripped from the substrate under the impact of the plasma plume, which is reflected by 0.14 V < RMS < 0.175 V; The processing mechanism of MD exists simultaneously in the same trajectory and adjacent trajectory scanning. When the mechanism of MD in the same trajectory scanning is most severe, RMS also reaches the maximum value of 0.23 V; The RMS has the same variation law as the material removal rate. When the removal rate reaches the maximum value of 12.2 × 107 μm3/s, the RMS curve also reaches its highest point, and the model prediction accuracy is greater than 89%.

Research on the detection of the hole in wood based on acoustic emission frequency sweeping

In order to detect the hole defect in the wood, the intrinsic frequency of wood is investigated by frequency sweeping and combining with pencil-lead break (PLB) tests to examine the influence of the hole defect on the intrinsic frequency to obtain the significant change index for non-destructive detection of the holes. Initially, the sweeping frequency test is conducted in the range of 10–180 kHz on two different sizes of Zelkova schneideriana and Pinus sylvestris var. mongholica specimens, followed by the signal's frequency domain analysis to determine the natural frequency and its range. Then, holes with diameters of 5, 10, and 20 mm are drilled on the Zelkova schneideriana specimens to explore the impact of hole diameter on the natural frequency using PLB and frequency sweeping. Finally, 1, 3, and 5 holes of 8 mm are drilled to investigate the influence of hole number on natural frequency. The results showed that the natural frequency of wood falls within two frequency ranges of 30–50 kHz and 150–180 kHz. In addition, the hole hinders the AE signal's propagation. The energy decay rate in the two natural frequency ranges increases as the diameter and number of holes rise. In addition to the traditional method for calculating signal propagation speeds, this study proposes effective measures for detecting holes in wood.

Properties of Amplitude Distributions of Acoustic Emission Signals Generated in Pressure Vessel During Testing

In the paper, the results of investigations on the properties of acoustic emission signals generated in a tested pressure vessel are presented. The investigations were performed by repeating several times the following procedure: an increase in pressure, maintaining a given pressure level, a further increase in pressure, and then maintaining the pressure at new determined level. During the tests the acoustic emission signals were recorded by the measuring system 8AE-PD with piezoelectric sensors D9241A. The used eight-channel measuring system 8AE-PD enables the monitoring, recording and then basic and advanced analysis of signals. The results of basic analysis carried out in domain of time and the results of advanced analysis carried out in the discrimination threshold domain of the recorded acoustic emission signals are presented in the paper. In the framework of the advanced analysis, results are described by the defined by the author descriptors with acronyms ADC, ADP and ADNC. Such description is based on identifying the properties of amplitude distributions of acoustic emission signals by assigning them the level of advancement. It is shown that for signals including continoues AE or single burst AE signals descriptions of such registered signals by means of ADC, ADP and ADNC descriptors and by Upp and Urms descriptors provide identical ordering of registered acoustic emission signals. For complex signals, the description using ADC, ADP and ADNC descriptors based on the analysis of amplitude distributions of recorded signals gives the order of signals with more accurate connection with deformational processes being sources of acoustic emission signals.

Experimental study on acoustic emission sensing and vibration monitoring of corroding reinforced concrete beams

Reinforcement corrosion is a major issue in reinforced concrete structures with severe societal and economic consequences if not detected and treated in time. This paper presents an elaborate experimental study to effectively monitor chloride-induced corrosion damage in reinforced concrete beams. A combined method of acoustic emission sensing and vibration-based monitoring is developed to detect, characterise and localise damage. Additionally, crack measurements on the concrete surface are performed as a reference technique. Four beams are corroded with an accelerated corrosion set-up while two additional beams are used as reference specimens. Whereas these monitoring techniques have already been applied individually to assess corrosion in reinforced concrete beams, they are here combined, in order to verify their complementarity. This results in an elaborate and innovative data-set which allows us to draw new conclusions on the way the techniques provide information on the evolution of corrosion damage in time and space. It was found that acoustic emission sensing can accurately detect and localise damage before cracking of the concrete, especially after dedicated noise filtering through clustering of the AE signals. Vibration-based monitoring is less sensitive to early damage, yet natural frequencies contain absolute information about the stiffness decrease due to corrosion damage, and strain mode shapes can localise damage on a larger scale. In addition, results from both techniques are in line with the crack measurements. As a result, the combination of these various monitoring methods offers valuable and complementary insights about the corrosion process.

Identification method for microseismic, acoustic emission and electromagnetic radiation interference signals of rock burst based on deep neural networks

Microseismic, acoustic emission, and electromagnetic radiation (MS, AE, and EMR) are promising methods for the monitoring and early warning of rock bursts. In the underground mining process, personnel activities and electromechanical equipment produce MS, AE, and EMR interference signals that affect the accuracy of MS, AE, and EMR monitoring. This study is aimed at overcoming the difficulties in identifying MS, AE, and EMR interference signals by means of on-site monitoring and deep learning methods. First, MS waveform signals and AE and EMR time series signals are collected by the SOS monitoring system and GDD12 monitor. According to the field records, the MS effective waveform, MS interference waveform, AE and EMR interference signals were labeled, and training sets and verification sets were made. An identification model for MS waveforms based on the ResNet-50 convolutional neural network and an identification model for AE and EMR interference signals based on the recurrent neural network were constructed using the original MS, AE, and EMR data. In addition, the identification accuracy and generalization ability of the models were analyzed. The results show that the proposed method can respond positively to MS, AE, and EMR interferences and accurately eliminate MS, AE, and EMR interference signals. It can significantly improve the reliability of MS, AE, and EMR monitoring data and effectively monitor rock burst disasters.

Determination of fracture toughness of 2.25Cr1Mo0.25V steel based on acoustic emission technique

The correct determination of fracture toughness is dependent on the accurate measurement of the point of crack initiation. In this research work, the AE technique is used to accurately detect the point of crack initiation and determine the initiation fracture toughness value JAE of 2.25Cr1Mo0.25V steel. Moreover, conventional standard procedures such as the J-R curve and stretch zone methods are also used to determine the fracture toughness JQ and JSZW values, respectively. Multiple AE parameters involving the entropy, peak amplitude, count, and energy are extracted from AE signals to identify the damage progression during fracture toughness tests. The results show several critical points associated with the point of plastic yielding, the onset of crack initiation, and the rapid crack growth and final fracture can be accurately identified by AE characteristics. Furthermore, the point of crack initiation recognized by AE is used to determine the JAE value. The fracture toughness JAE values of different specimens are also compared with the corresponding JQ and JSZW values. The JAE values are found to be about 20% lower than the JQ values but are significantly larger than the JSZW(2D) and JSZW(3D) values. It is believed that the fracture toughness parameter JAE determined by the AE technique can provide an important reference for evaluating the fracture toughness of engineering materials.

A Non-Destructive Measurement of Trunk Moisture Content in Living Trees Based on Multi-Sensory Data Fusion

Water plays an important role in various physiological activities of living trees. Measuring trunk moisture content (MC) in real-time without damage has important guiding significance for transpiration research in forest ecosystems. However, existing standing tree MC detection methods are either too cumbersome to install or cause different degrees of damage. Here, we propose a novel Internet of Things (IoT) monitoring system that includes wireless acoustic emission sensor nodes (WASNs) and underground soil MC sensor nodes to efficiently detect and diagnose the MC level of living tree trunks. After the characteristic parameters were collected by the two sensors, a feature selection and multi-sensory global fusion method for MC diagnosis was designed and developed and several statistical parameters were selected as the input variables to predict the heartwood MC level with a support vector machine (SVM) model. Moreover, to achieve the highest prediction accuracy, an improved sparrow search algorithm (ISSA) is applied to ensure the most suitable parameter combinations in a two-objective optimization model. Extensive experiments result in a fusion of the environment, and AE signals show that the proposed mechanism has better diagnostic performance than state-of-the-art methods and is more adaptable to the fluctuation of working conditions.

Research on friction state monitoring method of liquid film seal during start-up based on acoustic emission

This paper proposes a method based on acoustic emission to achieve nondestructive testing of the friction state of a liquid film seal. However, the acoustic emission signal is susceptible to noise, and extracting the feature signal is challenging. To address these issues, we propose a signal processing method based on singular value decomposition and adaptive variational mode decomposition (SVD-AVMD). Additionally, we investigate the acoustic emission mechanism of the liquid film seal using the strength theory of shear strain energy and CEB contact model. We design a liquid film seal friction experiment based on acoustic emission technology to extract the characterization signal of the friction state of the liquid film seal using SVD-AVMD. Finally, we apply a convolutional neural network to identify the friction state of the liquid film seal. The results demonstrate that SVD-AVMD has a significantly better ability to capture the center frequency of each mode component and to recover each mode component compared to simple variational mode decomposition. SVD-AVMD can filter out background noise while retaining the most useful information to the maximum extent possible. Furthermore, the root mean square of the extracted AE signal is consistent with the description of the AE mechanism, achieving the goal of studying and judging the friction state of the liquid film seal end face. Finally, we show that combining the convolutional neural network and acoustic emission time-frequency characteristics can improve the recognition accuracy of the friction state of the liquid film seal.

Acoustic emission identification of wheel wear states in engineering ceramic grinding based on parameter-adaptive VMD

In engineering ceramic grinding, the wheel wear states have an important influence on the processing efficiency and grinding quality. To address the difficulty of online monitoring of wheel wear states, a signal reconstruction method based on parameter-adaptive variational modal decomposition was proposed. Experiments on the entire life cycle of the grinding wheel were performed, and the wear states were classified based on the observed surface morphologies of the wheel and workpiece. Subsequently, acoustic emission signals were collected by nodes, and high correlation frequency bands were chosen and reconstructed. The signal features were extracted, and the wear states were identified based on a random forest optimization feature combination, finally. In this study, taking alumina ceramic as an example, eight groups of single-factor experiments were designed based on cumulative grinding depth to determine the optimal linear speed of the grinding wheel, worktable moving rate, and other process parameters. A total of 160 groups of AE signal samples were obtained. The results show that feature extraction by frequency bands can better reflect the changes in the grinding wheel wear states than the overall signal. In addition, optimization feature combinations based on the bands after decomposition, filtering, and reconstruction can divide and identify the various wear states more effectively. The comprehensive identification accuracy reached 90.6%. The accuracy of identifying the severe wear state reached 100%. Thus, the proposed method provides theoretical guidance for the practical industrial applications of online monitoring of wheel wear states.

Emicizumab: positive AE signals for arthralgia and injection-site pain, but also for some serious AEs

Emicizumab: positive AE signals for arthralgia and injection-site pain, but also for some serious AEs

Static and ultrasonic structural health monitoring of full-size aerospace multi-function capsule using FBG strain arrays and PSFBG acoustic emission sensors

In this study, we developed an integrated static and ultrasonic structural health monitoring (SHM), that was achieved using fiber Bragg grating (FBG) arrays and phase-shifted FBG (PSFBG) sensors, to monitor a full-size aerospace multi-function capsule under varying load. The FBG and PSFBG were bonded using direct and remote adhesive methods, respectively, to ensure the performance of the integrated SHM. The static strain measured by FBG accurately followed the loading curve during the initial 15 min of the test, and subsequently exhibited an irregular trend due to damage. Acoustic emission signals induced by damage were first detected by PSFBG at 15 min 11 sec. Following the detectable irregular strain and AE signals, the failure of the capsule manifested as a wrinkling ultimately occurred at 18 min. The FBG arrays and PSFBG sensors performed well for the integrated SHM and demonstrated that the newly proposed method can alert the unhealthy status of a large-scale aerospace structure before catastrophic failure.

EXPERIMENTAL ANALYSIS OF ACOUSTIC EMISSION PROPAGATION VELOCITIES AND ENERGY ATTENUATION LAW OF P AND S WAVES IN WOOD USING IMPROVED TDOA MEASUREMENTS

To explore the propagation law of AE signal in wood, the propagation velocity of P-wave and S-wave and the energy attenuation law of different frequency components were studied By PLB (pencil-lead break) tests. Firstly, an improvedtime-difference-of-arrival (TDOA)method was designed to determine the arrive time. Thepropagation velocities of P-wave and S-wave were calculated. Then, the Young's modulus was estimated by P-wave velocity. Finally, on thebasis of eliminating the influence of standing wave, the energy attenuation models were obtained by numerical fitting and wavelet decomposition. The results showed that the improved TDOA algorithm can calculate the propagation velocity of P-wave and S-wave at the same time through one test, and the P-wave velocity can be used to estimate the Young's modulus. P-wave propagated faster in soft wood, while S-wave propagated faster in hard wood. The higher thefrequency of AE signal, the faster the energy attenuation.

Acoustic Emission Characteristics and Damage Evolution of Sandstone with Different Pores under External Load

The rock will be damaged and destroyed when the external load reaches the bearing limit, which will be accompanied by complex AE signals and damage evolution laws. Therefore, in order to obtain the relationship between AE signal and damage evolution characteristics of rocks, 4 kinds of sandstones of a mine are used for AE test. Firstly, the porosity of 4 kinds of sandstone is tested. Secondly, the AE signal parameter characteristics of sandstone with different porosity are analyzed. Finally, the AE parameters obtained are combined with cellular automata and damage theory to analyze the damage evolution law and critical damage value of different sandstones. The results show that the pore size of the four sandstones is QSYX &gt; QSYZ &gt; FSYX &gt; FSYZ. The loading process is divided into compaction stage, elastic deformation stage, and plastic deformation stage, with peak strengths of 46.92 MPa, 43.32 MPa, 57.87 MPa, and 54.31 MPa, respectively. Or the AE event rate, the missing area, missing parts and missing number are different. The QSYX missing area is larger than QSYZ and FSYZ; the macrocrack growth speed is also faster; and the signs of fracture are obvious. The number of FSYX missing is more than QSYX, QSYZ, and FSYZ. The first two missing parts are caused by internal defects; the last two missing parts are signs of fracture; QSYX, QSYZ, and FSYZ are shear failure, and FSYX is tensile failure. The damage evolution process of the four sandstones corresponds to the loading process one by one. The calm stage of damage corresponds to the compaction stage, the damage expansion stage corresponds to the elastic deformation stage, and the damage acceleration stage corresponds to the plastic deformation stage. The critical damage values are 0.438, 0.499, 0.576, and 0.476, respectively, which are higher than the critical damage values of the sandstone cell model of 0.43, indicating that when the damage values reach the critical value, instability exists and instability failure will occur with continuous load.

Evaluation of grinding characteristics for sapphire ultra-precision grinding using small grit sizes wheels based on AE signals

This paper focused on the grinding characteristics for sapphire ultra-precision grinding with small grit size grinding wheels and acoustic emission signal monitoring of material removal mechanism under the action of multiple abrasive grain. The results show that the use of small grit size grinding wheels allowed for effective removal of sapphire and they facilitated the production of ductile surfaces at the correct parameters. The grinding depth was the most significant effect on the grinding characteristics, the grinding force and subsurface damage scale increased with the grinding depth, and the grinding surface morphology developed from ductile surface to low damage ductile surface and brittle fracture surface. The material removal behavior can be monitored with acoustic emission signals, the original signal, frequency domain and wavelet decomposition features were distinct attributed to the different material removal modes. Besides, low-scale subsurface damage also occurred at the parameters of small size grits and minimal grinding depth.

Monitoring of ductile–brittle transition mechanisms in sapphire ultra-precision grinding used small grit size grinding wheel through force and acoustic emission signals

Progressive ultra-precision grinding experiments used small grit size grinding wheels were performed to investigate the ductile–brittle transition process of sapphire, force and AE signals were employed to monitor the material removal behavior. Precision dressing and measurement of circular arc grinding wheels were performed to meet the minimum grinding depth for ultra-precision grinding. A grinding force model was proposed to interpret the grinding forces in different materials removal modes. A continuous complete ductile–brittle transition process was observed and monitored, the progressive grinding was divided into three stages: ductile dominant, ductile–brittle transition and brittle eruption. The grinding force, surface profile, micro morphology and brittle fracture percentage were discussed. Brittle fracture will not entirely occur, but rather brittle fracture and ductile grooves were coexisted on the surface in the brittle eruption stage, which was attributed to the grinding characteristics of small grit wheels. In addition, raw AE signals and multiple transformation forms were utilized to monitor the material removal behavior and their respective roles were analyzed. The Fourier transform, discrete wavelet, continuous wavelet and wavelet coefficients in different stages can all establish a mapping relationship with material removal modes.

Investigation of energy absorption capacity of 3D filament wound composite tubes: experimental evaluation, numerical simulation, and acoustic emission monitoring

By analyzing the failure mechanisms, crashworthiness characteristics of FW composite tubes subjected to two modes of progressive damage and catastrophic failure are investigated using acoustic emission technique and numerical method. The AE signals of ±45° composite tubes were classified using hierarchical and wavelet transform methods, and based on the realistic and three-dimensional geometrical architecture of tubular structures, the microstructural finite element model was developed using Catia and ABAQUS software. Then deformation patterns and the impression of each mechanism on the crashworthiness characteristics were assessed. Results indicated that fiber breakage and fiber/matrix debonding could likely control the higher percentage of damage. By changing the type of modes from progressive damage to catastrophic failure, the percentage of matrix cracking increases, the fiber/matrix separation decreases, and the failure behavior become dominated by local buckling. Comparing the FE simulation with experimental results, we found the proposed 3D model can reasonably predict the pre-crushing, post-crushing, and material densification.

A computational study of AE signal due to fiber break and fiber matrix debonding in model composite

Acoustic emission monitoring is a useful method to deal with detection and identification of damage in composite materials during mechanical tests. From an experimental point of view, it is not straightforward to establish a quantitative link between the damage sources and AE signals. Modeling and numerical simulations appear as a promising way to reach this objective. In this study, Finite Elements Modeling (FEM) was used to simulate AE signals due to fiber break and fiber/matrix debonding in a model carbon fiber composite. A specimen made of a single carbon fiber and epoxy matrix was used to experimentally validate the simulated fiber break AE signals. The objective was to compare the AE signals from a validated fiber break simulation (Fig. 1a) to the AE signals obtained from fiber/matrix debonding (with several models) and fiber break obtained in several media and to discuss the capability to detect and identify each source (Fig. 1b). The influence of the type of sensor, specimen geometry and AE source location were also studied. The proposed model was extended to identify the main parameters that influence the acoustic emission signatures. These validated results open the way to combine the experimental and the simulated data to generate a library in order to identify real-time damage mechanisms. Fig. 1: (a) Amplitude and roll-off frequency vs. source/sensor distance for experimental signals and numerical signals. (b)Evolution of Partial Power PP1 [0–125 kHz] as a function of amplitude obtained with a nano 30 sensor for several sources. Fiber break and debonding (L = 20 μm or 100 μm). EWGAE 35, Ljubljana, Slovenia, 13th – 16th Sep. www.ewg

Characterization of optical ae signals at loading of fibre reinforced composites

Characterization of acoustic emission (AE) signals in loaded materials can reveal structural damage and consequently provide early warnings about product failures. Therefore, extraction of the most informative features from AE signals is an important part of the characterization process. This study considers the characterization of AE signals obtained from bending experiments for biocomposite and glass fiber epoxy (GFE) composites at room temperature and low temperature. For the acquisition of AE signals, fiber optic sensors (FOS) are used that can outperform classical electrical sensors under challenging operational environments. In this paper, we propose the extraction of deep features using different machine learning methods. The deep features are compared with extracted standard AE features. Then, the different feature sets are analyzed through discriminant analysis, neural networks, and extreme learning machines, combined with feature selection, to estimate the predictive potential of various feature sets. The proposed signal processing structure is focused on the classification of AE signals to recognize the source material, evaluate the predictive importance of extracted features, and evaluate the ability of used FOS for evaluation of material behavior under challenging low-temperature environments.

Experimental characterization, modelling and simulation of ae signal due to crack propagation in PMMA

Acoustic Emission (AE) is a non-destructive instrumentation that allows the detection of cracking, which dissipates elastic waves resulting from local crack-induced displacement jumps within the material. However, based on the AE experiments, a link between the crack fracture mechanics and the AE features is difficult to establish quantitatively, especially under quasi-static and dynamic loading conditions. Numerical simulations have proved to be very promising for this purpose. In this study, Pencil-Lead Break (PLB) tests are carried out on PMMA plate specimens with different thicknesses in order to determine the damping characteristics of PMMA and experimentally validate the AE numerical simulations. The influence of the specimen thickness, the distance between the sensor/source and the type of sensors on the measured acoustic emission are also studied.