Acoustic Emission Signal Propagation Research Articles

Experimental study of the generation and propagation of acoustic emission signals in laser micro welding

Experimental study of the generation and propagation of acoustic emission signals in laser micro welding

Development of a Semi-Autonomous Pulse and Receive Concrete Inspection System

Concrete structures are being subjected to increasing loads and used beyond their intended lifespan. When combined with operators shrinking maintenance budgets it is imperative structures are monitored for damage. Acoustic Emission (AE) and AE Tomography offer a method for damage detection and characterisation. However, they are encumbered by their equipment and setup requirements, and best used on areas where damage is already known to have occurred. An autonomous pulse catch system could be used to identify these areas. To explore the potential of such a system, seven concrete beams differentiated by their aggregate size were tested using an automated pulse and receive system. The effect of aggregate size, and pulse frequency on attenuation and wavespeed are investigated. The pulse regime consisted of narrowband pulses ranging from 30 kHz to 500 kHz. The results show that high frequency pulses experienced greater attenuation than low frequency pulses. Whilst a link appears between the size of the aggregate and the rate of attenuation of high frequency signals. A strong negative correlation between amplitude loss and pulse frequency was observed. For the specimen containing coarse aggregate, pulse velocity was constant between 100 kHz and 500 kHz, whereas specimens containing coarse aggregate experienced frequency dependant attenuation. This testing shows the significant potential for an automated pulse and receive system, which could lead to the development of an autonomous health monitoring system. In addition, the approach can be instrumental in developing large data sets, that can be used in Machine Learning processes, to develop a deeper understanding of AE signal propagation in concrete materials.

Research on features and localization of AE signals from the mechanical body of industrial robots based on WD-EMD

Abstract In order to reveal the propagation characteristics of acoustic emission (AE) signals in the body of industrial machinery, the characteristic frequencies and wave speeds of AE signals propagated on the interior and exterior surfaces (IS and ES) of the body were extracted. Subsequently, an algorithm utilizing characteristic frequency and time difference of arrival (TDOA) is proposed for the identification and localization of AE sources. Initially, an AE source is induced on the IS and ES of the body using the pencil-lead break method in accordance to ASTM standards, and the AE signal is captured by two piezoelectric sensors at a sampling frequency of 3 MHz. Then, to avoid the limitations of wavelet decomposition using self-selected wavelet scale and the problem that a single indicator cannot properly evaluate the correlation between independent mode functions (IMFs) with the original signal, this paper will conduct 4-layer wavelet decomposition of the original signal according to the response frequency range of the sensor, and select the wavelet details within the stable range of the response frequency of the sensor for preliminary reconstruction,and then the empirical mode decomposition (EMD) method is used to decompose the de-noised AE signal into 7 IMFs, and the AE waveform is reconstructed by the combined information of correlation coefficient and variance accounted for. Secondly, the reconstruction method combined with EMD analysis and a single index is compared with the proposed method in this paper to verify the reliability of the proposed method. In addition, the frequency domain characteristics of AE signal propagation process on the IS and ES of the body are extracted. Finally, based on the TDOA principle, the propagation speed of AE signal on the IS and ES of the body is calculated. Based on the geometric relationship between the AE source and two sensors, an algorithm for the location of the AE source is proposed. The results show that the proposed signal reconstruction method can effectively extract the features of AE signals, and the average positioning accuracy of the localization algorithm based on characteristic frequency and TDOA reaches 0.86%.

An end-to-end framework based on acoustic emission for welding penetration prediction

The adverse effects of inadequate welding penetration on the service performance of welded assemblies have raised significant concerns. In this study, we explore the benefits of acoustic emission (AE) sensors for monitoring the laser-arc hybrid weld penetration state during welding processes. The underlying mechanisms of AE signal generation under different penetration states are elaborated, and the feasibility of using time-frequency domain features for monitoring penetration state is validated. Building upon these findings, we propose an end-to-end framework named WAENet. This framework incorporates an automated optimization module for signal processing and time-frequency domain feature extraction, as well as an improved modular convolutional neural network (CNN) for recognition. To enhance the CNN's performance, techniques such as grouped convolution, depth-wise separable convolution, and global average pooling are employed. Furthermore, the training process of the CNN also considers the involvement of hyperparameters related to signal processing and feature extraction, as proposed in this article. WAENet achieves an average accuracy of 99.62 % in identifying the penetration states, which outperforms other models that use alternative feature extraction or classification techniques for comparison. These studies expand the application scope of AE and CNN in the field of intelligent welding.

STUDY ON ACOUSTIC BLACK HOLE EFFECT OF ACOUSTIC EMISSION SIGNALS IN PINUS SYLVESTRIS VAR. MONGOLICA LITV

The difference in density and wave velocity causes distinct wave impedance between air and wood, resulting in complex acoustic emission (AE) signals due to reflection on the wood's surface. This study explores the suppression of AE signal reflection by modifying the structure of thin wood panels, utilizing the theory of acoustic black holes (ABH). Initially, aone-dimensional ABH structure was created by forming a wedge structure on one side of thespecimen. Pencil-lead break (PLB) tests simulated sudden AE sources on the specimen's surface. AE signals were collected using three equidistant sensors on the upper surface, with asampling frequency of 2MHz. The AE signal was then segmented into frequency bands using the differential method and analyzed in both time and frequency domains. Comparisons were made to understand the impact of the one-dimensional ABH on AE signal propagation. Results demonstrated that the one-dimensional ABH effectively suppressed AE signal reflection on thewood's surface, reducing the high-frequency components by 18.31%, 20.83%, and 12.09% for each sensor, respectively. Furthermore, the experimental cut-off frequency of 0.98 kHz surpassed the theoretically calculated value of 0.39 kHz due to the disparity between the ABH structure's thickness and the theoretical prediction.

Failure Severity Prediction for Protective-Coating Disbondment via the Classification of Acoustic Emission Signals.

Structural health monitoring is a popular inspection method that utilizes acoustic emission (AE) signals for fault detection in engineering infrastructures. Diagnosis based on the propagation of AE signals along any surface material offers an attractive solution for fault identification. However, the classification of AE signals originating from failure events, especially coating failure (coating disbondment), is a challenging task given the AE signature of each material. Thus, different experimental settings and analyses of AE signals are required to classify the various types of coating failures, and they are time-consuming and expensive. Hence, to address these issues, we utilized machine learning (ML) classification models in this work to evaluate epoxy-based-protective-coating disbondment based on the AE principle. A coating disbondment experiment consisting of coated carbon steel test panels for the collection of AE signals was implemented. The obtained AE signals were then processed to construct the final dataset to train various state-of-the-art ML classification models to divide the failure severity of coating disbondment into three classes. Consequently, methods for the extraction of useful features, the handling of data imbalance, and a reduction in the bias of ML models were also effectively utilized in this study. Evaluations of state-of-the-art ML classification models on the AE signal dataset in terms of standard metrics revealed that the decision forest classification model outperformed the other state-of-the-art models, with accuracy, precision, recall, and F1 score values of 99.48%, 98.76%, 97.58%, and 98.17%, respectively. These results demonstrate the effectiveness of utilizing ML classification models for the failure severity prediction of protective-coating defects via AE signals.

Propagation characteristics of acoustic emission signals in solid-liquid-solid coupling interface

The propagation path of acoustic emission (AE) signal contains several structures with solid-liquid-solid coupling interfaces. In this paper, the effect of the coupling interface on the AE signal propagation is investigated. Based on the theory of Lamb wave, a solid-liquid-solid coupling interface model is designed. The propagation characteristics of the AE signals with different frequencies are predicted by simulation in the coupling models with different oil film thicknesses and contact areas. Furthermore, experimental verification is carried out. The results of the experiments are consistent with the numerical simulations. The results indicate that the oil film thickness and the center frequency will have an effect on the propagation of AE signal. Two passing coefficients are proposed to quantify the propagation characteristics. This paper provides guidance for the study of the AE signal propagation characteristics in the complex structure with multi-media interfaces.

Study on the Variation Laws and Fractal Characteristics of Acoustic Emission during Coal Spontaneous Combustion

Acoustic emission (AE) technology has the advantage of online localization to study the change laws of AE in the process of coal spontaneous combustion and to reveal the generation mechanisms of AE signal during the process of heating and rupture of coal body from a microscopic perspective. This paper first constructs a large-scale coal spontaneous combustion AE test system and conducts experimental tests on the AE signal in the process of coal spontaneous combustion. The results show that with the increase of temperature in the process of coal spontaneous combustion, the AE signal shows a trend of increasing fluctuations. Low-temperature nitrogen adsorption experiments studied the pore structure of coal spontaneous combustion, and the results showed a correspondence between the development of pores and the temperature of coal spontaneous combustion. Further, through the analysis of the evolution of the pore structure of coal through Fourier transform and fractal theory, it is found that the high-frequency main frequency AE signal and average frequency are continuously enhanced with the increase of temperature. The fractal dimension of the pore structure and the fractal dimension of the AE count of the coal body first rise and then decline. The mechanism of coal spontaneous combustion AE of coal is revealed, and the pore development caused by thermal stress when coal heats up is the main source of AE signal generation. The research in this paper is of great significance for applying AE technology to detect the position of coal spontaneous combustion.

Acoustic emission source localisation for structural health monitoring of rail sections based on a deep learning approach

An acoustic emission (AE) approach for non-destructive evaluation of structures has been developed over the last two decades. In complex structures, one of the limitations of AE testing is to find the location of the AE source. Time of flight and wave velocity are typically employed to localise AE sources. However, complex rail structures generate multiple wave modes travelling at varying speeds, making localisation difficult. In this paper, the challenge of localisation has been split into two parts: (a) identification of the AE source zone, i.e. head, web or foot, and (b) identification of location along the length of the rail. AE events are simulated using a pencil lead break (PLB) as the source. Three models including an artificial neural network and 1D and 2D convolutional neural networks (CNNs) are trained and tested using AE signals generated by PLB sources. The accuracy of zone identification is reported as 94.79% when using the 2DCNN algorithm. For location classification it is also found that 2DCNN performed best with 73.12%, 79.37% and 67.50% accuracy of localising the AE source along the length in the head, web and foot, respectively. For AE signal generation from actual damage in a rail, a bending test on an inverted damaged rail section was then performed with loads of 100 kN, 150 kN and 200 kN. For all loads, the 2DCNN model resulted in accurate prediction of the zone of the AE source, and it accurately predicted the AE source location along the length for the loads of higher intensity (150 kN, 200 kN). It is envisaged that the deep learning approach presented in this research work will be helpful in developing a real-time monitoring system for rail inspection based on AE.

Experimental study on mechanical properties of single fracture-hole red sandstone

Various fractures and holes in the natural rock mass affected the mechanical properties of the rock mass and the safety construction of engineering. In this study, we investigated the mechanical properties of a single fracture-hole rock specimen using particle flow code 2D (PFC2D) numerical simulation software and through laboratory tests. We analysed the failure behaviours and mechanical properties of the rock specimen with a single fracture-hole specimen under different fracture angles. The failure modes of single fractured rock samples with different fracture angles were revealed. The fracture propagation and stress evolution of the rock specimen with a single fracture-hole under different fracture angles were investigated. The experimental results shown that the peak strength, peak strain, elastic modulus, initial fracture stress, and damage stress of the single fracture-hole rock specimen with different fracture angles were significantly less than those of the intact rock specimen. Moreover, fracture hole defects accelerated the generation of fractures and promote the failure of the rock specimen. The failure modes were divided into Y, inverted Y, and V types. Before the rock specimen fractures, the stress concentration area was mainly distributed at both ends of the fracture. The stress concentration area at both ends of the fracture gradually decreased, and the stress concentration area near the hole gradually increased as the fracture angle increased. By experiments, the acoustic emission of the model had gone through three stages: initial, steady growth, and rapid decline. The size of the inclination angle affected the number of acoustic emission hits and the generation of acoustic emission signals. Failure behaviours of the rock specimen with a single fracture-hole were systematically investigated, which could promoted the development of fracture rock mechanics and improved the understanding of instability failure mechanism in rock engineering, such as nuclear wasted treatment engineering and deep underground engineering.

Experimental and Theoretical Study of Plastic Deformation of Epoxy Coatings on Metal Substrates Using the Acoustic Emission Method.

Propagation of acoustic emission signals in continuous conjugated media under real-time loading was explored. The results of explored plastic deformation polymer coatings on a metal base using the acoustic emission method with synchronization of deformations and the moments of occurrence of acoustic emission signals are presented. Using the principal component method, the acoustic emission spectra, which make it possible to trace the evolution of deformation transformation processes, were analyzed. Presented the results of theoretical and experimental studies on the separate propagation of acoustic emission vibrations in a polymer coating, a metal base, and their joint combination in the form of multilayer structures. Boundary problems of propagation of acoustic emission signals in the conjugation of continuous media are considered from the standpoint of an elastic continuum and wave representations. The main variables are the force that initiates the appearance of acoustic emission signals and the displacement that determines the propagation of elastic waves. Based on the local rearrangement of the internal structure of conjugated media under conditions of development of deformation processes in the material, the verification of the main theoretical models of energy spectrum acoustic signals in continuous media at the micro-, meso-, and macro-levels was carried out. In this work, we present experimental data on a set of basic acoustic emission characteristics for four-point bending. It is shown that the principal components method reduces the dimension of data while maintaining the least amount of new information. Using the method of principal components to determine the stages of plastic deformation of polymer coatings on a metal base using the acoustic emission method. With the digitalization of acoustic emission signals and noise filtering, new possibilities for isolating a weak signal at the noise level appear even when its amplitude is significantly lower than the noise level. The study results can be used to predict the degree of destruction of two-layer materials under loading.

Modified Model of Sound Velocity with Different Saturation in Fractured Sandstone

The hazards of surrounding rock sheeting, collapse and rock explosion during the excavation of underground projects can be regarded as the macroscopic dynamics of the evolutionary development of their internal fractures, mostly accompanied by acoustic emission phenomena. The application of acoustic emission detection technology can quickly determine the existence of fissures in the surrounding rock and predict their approximate location and spatial spread. Therefore, considering the effect of fissures on the sound velocity propagation law. In this work, experiments on the identification of acoustic emission signal paths in solid media with different void states are carried out, and the path propagation law of acoustic emission signals is explored and studied. A comparative analysis of acoustic emission source localization in fractured sandstone with different sensor arrays at different saturation levels was carried out using water as the coupling agent. The acoustic emission source 3D localization results are optimized by correcting the time difference model. The results show that the acoustic emission signal propagation conforms to the shortest distance principle. In the localization of 3D cylindrical AE sources, it is suitable to select a combined array of spatial tetrahedral sensors for better localization. As the saturation increases the positioning effect gets closer to the actual value. The sound source localization effect of the sound velocity correction model based on the time difference method is closer to the actual lead break position. In actual engineering, water as a benign coupling agent can better improve the accuracy of AE source localization in fracture-containing sandstone, which can provide some guiding suggestions for related engineering.

Total hip replacement monitoring: numerical models for the acoustic emission technique.

Any mechanical instability associated with total hip replacement (THR) excites elastic waves with different frequencies and propagates through the surrounding biological layers. Using the acoustic emission (AE) technique as a THR monitoring tool provides valuable information on structural degradations associated with these implants. However, several factors can compromise the reliability of the signals detected by AE sensors, such as attenuation of the detected signal due to the presence of biological layers in the human body between prosthesis (THR) and AE sensor. The main objective of this study is to develop a numerical model of THR that evaluates the impact of biological layer thicknesses on AE signal propagation. Adipose tissue thickness, which varies the most between patients, was modeled at two different thicknesses 40mm and 70mm, while the muscle and skin thicknesses were kept to a constant value. The proposed models were tested at different micromotions of 2µm, 15-20µm at modular junctions, and different frequencies of 10-60kHz. Attenuation of signal is observed to be more with an increase in the selected boundary conditions along with an increase in distance the signals propagate through. Thereby, the numerical observations drawn on each interface helped to simulate the effect of tissue thicknesses and their impact on the attenuation of elastic wave propagation to the AE receiver sensor.

Estimation of the additive and multiplicative error of the standard algorithm of acoustic emission sources linear location

The paper deals with the results of the standard acoustic emission (AE) source linear location algorithm, which evaluates the location of developing damage in a steel sample under static load test to destruction. The linear location algorithm error is due to many factors: acquisition threshold of the AE signals and the dispersion properties of recorded signals, in particular, the dependence of the AE signal propagation velocity on its amplitude. According to the test results, the additive error in determining the time difference of arrival (TDA) of the AE signals to the transducers array is 52.8 μs and the average multiplicative error level is 0.67.

Experimental investigations into the propagation of acoustic emission signals from particle impacts along a waveguide

On-line particle sizing through acoustic emission (AE) sensing is promising in many industrial applications. However, very limited research on the fundamental sensing mechanism of this technique is available. One imminent issue is the propagation of impact-induced AE signals along a waveguide, which plays a significant role in interpreting the resulting acoustic waveforms and optimizing the design of the waveguide. In this paper, a broadband point-contact sensor, which has an extremely flat frequency response, is used to acquire faithful AE signals. Experimental investigations into the wave propagation behaviour are presented and the influences of different parameters, including particle size, impact velocity, material type and impact location, on the detected AE signals are also discussed. Two fundamental waves with different characteristics are observed in the acoustic waveforms. Experimental results obtained demonstrate that both the particle size and material type greatly affect the amplitude rather than the signal pattern of the acoustic waveform. Meanwhile, significant changes in the signal amplitude and pattern occur with different impact velocities and impact locations.

Study on the Mathematical Model and Propagation Characteristics of AE Waveform Signals during Rock Fracture

Rock deformation or fracture is accompanied by the phenomenon of acoustic emission (AE). Due to the heterogeneity and anisotropy of rock materials as well as the complexity of their fracture, AE signals recorded by sensors at different positions have different characteristics. To explore factors influencing these differences, this study examines the effects of the physical properties of rocks, such as heterogeneity, anisotropy, and viscosity, on AE waveform signals from the perspective of the rock material and its fracture characteristics as well as the characteristics of the propagation of different AE waveform signals. The results show that the frequency (f) of the AE signals generated by rock fracture is inversely proportional to crack length (c) and directly proportional to the rate of crack growth (v). During signal propagation, the comprehensive effects of such factors as the heterogeneity, anisotropy, and viscosity of rocks as well as environmental noise weaken the energy of the signals and enhance the distribution of signal frequency. Each factor differently influences the time frequency of AE. A model for the propagation of AE signals was built and verified. Finally, as for on-site rock mass engineering, the low-frequency signals should be analysed prior to analysis in rock mass disaster monitoring.

High-Speed In Situ Study of the Correlation between the Deformation Bands Formation and Acoustic Response in Al–Mg Alloy

The dynamics of deformation bands in Al–Mg alloy has been investigated synchronously in situ by two methods: acoustic emission (AE) and high-speed (20 000 fps) video recording of a sample surface deformed at a specified stress rate $${{\dot {\sigma }}_{0}}$$ . It is established that a band nucleates at a random site on the lateral surface of planar sample and grows in the form of a needle-shaped embryo band at an angle of about 60° with respect to the extension axis, with a tip velocity of up to ~10 m/s. The embryo-band nucleation, growth, and outcrop onto the opposite lateral surface is accompanied by generation of a broadband AE signal in the frequency range of ~102–106 Hz. The low-frequency component of the AE signal in the range of ~0.1–1 kHz is related to the macroscopic band behavior, and the high-frequency component in the range of ~0.1–1 MHz contains data on the mesoscopic events, related to jumps in the velocity of embryo-band tip and its outcrop onto the external sample surface. The mechanisms of AE signal generation have been discussed.

Boundary-Value Problems of Determining the Energy Spectrum of Acoustic Emission Signals in Conjugate Continuous Media

The boundary-value problems of propagation of acoustic emission signals with the conjugation of two continuous media are considered. The main variables in the problems are the force that determines the origin of acoustic emission and displacement of particles of the medium that causes origin and propagation of elastic waves. The methodological substantiation of the solution of the boundary-value problem in conjugate media using the Green function and Fourier transforms is presented. The energy spectrum of acoustic emission is shown to be completely determined by the force constants of the material and forces that initiate the origin of acoustic emission signals.

Experimental evaluation of the effect of carbon fibres on acoustic emission parameters obtained during compressive strength tests of alkali-activated slag composites

The generation of acoustic emission signals is directly associated with formation of cracks in materials during loading. This paper deals with possibilities of acoustic emission method application as the tool for the identification of structural damage in alkali-activated composite materials during compressive strength test. In experimental part, the three piezoelectric sensors were occupied for the continuous record of emission signals of stressed material feedback on applied mechanical load in real time. Detection of specific acoustic emission signals in the course of deformation of the test samples indicates that irreversible structural changes occur in the composite. Four different mixtures of alkali-activated slag mortars were prepared, the first one was reference without the addition of carbon fibres. The others contain the carbon fibres in amount 0.5, 1.0 and 2.0 % from the weight of the slag.

Experimental wavelet analysis of acoustic emission signal propagation in CFRP

In this research work, the characteristics of acoustic wave propagation were studied for different Carbon Fiber/Epoxy laminated composites (CFRP). Piezoelectric transducers were used to simulate the acoustic wave propagation through the materials and the possible effects of ply number, fiber orientation and the material thickness of the CFRP on the propagation of acoustic signals were studied. The relative loss of the Acoustic Emission (AE) signals has been parameterized in terms of variations in the peak amplitude, acoustic energy and duration of the recorded acoustic signals. The effect of the material properties on the relative loss in AE descriptors has been assessed. Waveform analysis was performed for the simulated and received acoustic signals to identify changes in amplitude and waveform. Wavelet analysis for the recorded acoustic waveforms were performed by the Continuous Wavelet Transform. The effect of material properties on the propagation of the acoustic signal was characterized through AE descriptors, Waveform and Wavelet Analysis.