Acoustic Emission Frequency Research Articles

Study on dynamic splitting tensile damage characteristics of basalt fiber reinforced concrete based on AE and DSCM

This paper aims to study the action mechanism of basalt fiber on concrete and the internal damage evolution of basalt fiber reinforced concrete (BFRC) in Brazilian disc splitting tensile tests using acoustic emission (AE) technology and digital speckle correlation method (DSCM). The effects of loading rate and basalt fiber content on splitting tensile failure mode and deformation capacity of BFRC, as well as AE frequency domain characteristics were investigated herein. Then the strain field and local damage factor of BFRC under dynamic splitting tensile load were analyzed based on DSCM. The results indicate that the increase of loading rate and basalt fiber content can change the failure into a complex tensile-shear mixed failure. And the deformation capacity of BFRC can be considerably enhanced by adding a proper amount of basalt fibers. It is also found that the damage degree and damage mechanism of BFRC can be identified by analyzing the wavelet peak frequency of specific frequency range. Furthermore, the crack evolution of BFRC under dynamic splitting tensile load can be reflected well on the change of strain field nephogram of BFRC obtained by DSCM.

A study on the FRP composite’s matrix damage resistance by means of elastic wave propagation analysis

In the study, selected materials used for fiber reinforced polymer (FRP) composites’ matrices were subjected to Compact Tension (CT) test. During the test acoustic emission (AE) parameters were registered which let to introduce AE technique as a valuable and helpful method to monitoring and identifying polymer failures. Gathering of elastic waves propagation data was conducted with AMSY-5 acoustic emission system produced by Vallen Systeme GmbH, in accordance with Vallen’s user manual. Next, the analysis was done using dedicated software based on Fast Fourier Transform (FFT) and Wavelet Transform (WT). Four cured epoxy resins with different chemical composition were chosen to the investigation. Epidian 5 and Epidian 53 epoxy resins were hardened using the two following curing agents: Z1 triethylenetetramine curing agent and PAC polyaminoamide curing agent. The paper was prepared as a continuation of authors’ previously conducted research on evaluating FRP composites’ matrices strength and damage growth resistance, for the same four materials. Obtained AE results were then discussed in comparison to mechanical tests results, such as fracture toughness and stress/strain characteristics. Therefore, some interesting relations were observed between AE frequency and number of hits/counts which were affected by material composition and its fracture toughness. Moreover, microscopic analysis was conducted to present phenomena appeared on fracture surfaces of broken specimens, including microcracks, “crack arrest” as well as so called river patterns.

Acoustic emission based grinding wheel wear monitoring: Signal processing and feature extraction

Surface grinding, being one of the later stages of a manufacturing process, is crucial to achieve the desired workpiece quality by maintaining proper wheel condition. Machine learning and acoustic emission (AE) detection have demonstrated potentials for developing an effective on-line wheel wear monitoring system. However there are inconsistencies among existing studies regarding to signal sampling and feature extraction methods. In the present paper, surface grinding experiments using medium carbon steel workpieces were conducted to determine the effects of using different signal analysis window lengths, feature types, and AE sensor bandwidths on the wheel wear detection performances. In addition, the use of AE transient spikes occurred during wheel entry and exit were analyzed. Feature were extracted from time domain (TD), frequency domain (FD), and time–frequency domain (TFD) representations of the measured AE signals, which were then refined using sequential floating forward selection (SFFS) algorithm to reduce feature redundancy. Support vector machine (SVM) algorithm with linear kernel was used for pattern classification. A number of insights for future monitoring system development were obtained. Firstly, while the lower frequency AE sensor (below 100 kHz) provided adequate classification performances, the higher frequency AE sensor (above 100 kHz) produced more robust results. Secondly, 100% classification rate was achievable using features derived from Fast Fourier Transform (FFT) in combination with descriptive statistics. Thirdly, it was determined that a window length as short as 1 ms (1000 data points) could capture enough signal information using FFT with the higher frequency AE sensor. Lastly, the choice sampling instance did not significantly impact classification outcomes.

Experimental and numerical investigation on interlayer fracture process of roller compacted concrete

A roller-compacted concrete (RCC) is a dry concrete with zero slump, and it is constructed in layers. The interlayers of RCC dams are generally weak. The fracture properties of the RCCs affect the performance and safety of the dam structures. Herein, fracture tests on RCC three-point bending beams were conducted, and the fracture toughness and energy of the RCC interlayers under different time intervals and interlayer treatments were studied. The acoustic emission (AE) characteristics of the interlayer fracture behaviours were analysed. Additionally, a bond-based quasi-brittle material interface (BQMI) model for peridynamics was constructed for interlayer fracture analysis, and the numerical results of the interlayer fracture process of the RCCs were compared with the test results. The results proved that longer interval times lowered the fracture toughness and energy. Moreover, interlayer roughening and cement mortar addition improved the interlayer fracture properties. Crack initiation and unstable failure could be identified or predicted by the abrupt changes in the AE hits and accumulative absolute AE energy, the appearance of AE time–frequency quiet period, and changes in AE frequency distribution and dominant frequency ratio. Furthermore, the peridynamic numerical simulation results were in good agreement with the RCC test results, demonstrating the reasonableness and effectiveness of the BQMI model. This method shows good performance in reconstructing the entire fracture process of the RCC interlayers.

Study of the Healing Effect of Concrete with Supplementary Cementitious Materials after Early-Age Damage by Acoustic Emission Technique

The study on the influence of early age damage of concrete on its long-term strength development is of great importance. In this work, 102 concrete cubes with and without supplementary cementitious materials (SCMs) were prepared. The pre-loading with loading degrees of 20%, 50%, and 80% of the corresponding compressive strength at 3-, 7-, 14-, and 28 d age was applied to the concrete samples. Then, concrete samples were further cured to 270 d, and the compressive strength was tested by the uniaxial compression test. The acoustic emission signals during the compressive strength test were collected. It is found that the pozzolanic reaction healed the damage caused by the early age damage, and the compressive strength of concrete with the incorporation of SCMs at 270 d age after pre-loading was higher than that of ordinary concrete without SCMs. The peak frequency of the uniaxial compression acoustic emission of concrete can be divided into four frequency intervals to correspond to different damage mechanisms of concrete, namely: interval I (12 ± 5 kHz), interval II (38 ± 5 kHz), interval III (171 ± 5 kHz), interval IV (259 ± 5 kHz).

Investigation on Acoustic Emission Characteristics of Hole-Joint Contained Granite under a Compressive Disturbance: Experimental Insights

Abstract Uniaxial compression experiments were performed in cross-jointed granite samples with a hole, together with acoustic emission (AE) observations, to investigate AE variability and precursory information during sample failure. The variability characteristics of multiple AE time-series parameters (including AE counts, accumulated counts, energy, accumulated energy, and b-value) and frequency domain parameters (including AE frequency and amplitude) were systematically analyzed, the variations in different parameters and precursor characteristics were compared, and the AE parameters suitable for rock failure prediction in a cavern were discussed. The results indicate that the variability characteristics of different AE parameters are different, and each parameter has a precursor phenomenon of specimen fracture. The peak value of specimen AE counts does not necessarily appear at the peak stress, but the AE counts decrease significantly when the samples are damaged. The accumulated AE count curve makes it easier to identify the precursors of sample failure. The AE energy is released at an accelerated speed when the samples are near complete failure, and a critical energy release exists in the samples. The abrupt increase in AE energy has potential as a precursor to sample failure. The b-value shows a fluctuating decline during the whole loading process and a rapid decline before sample failure. The low-frequency with high-amplitude and the high-frequency with low-amplitude signals exhibit distinct characteristics at different specimen deformation stages and are sensitive to the change in specimen stress state, so they can be used as the crucial signal source for predicting specimen failure. The joint analysis of these AE parameters can better capture the precursory information of rock failure.

Acoustic emission-based process monitoring in the milling of carbon fibre-reinforced plastics

Milling of fibre-reinforced plastics is a challenging task. The highly abrasive fibres lead to high tool wear and coating failures, which cause increasing process forces and temperatures. Machining with a worn tool, in turn, can result in unwanted workpiece damages such as delamination or fibre protrusion. Reliable monitoring of the process must therefore be able to detect damages to the milling tool and the workpiece alike. The presented process monitoring approach measures the acoustic emission generated by the milling tool cutting edge entering the workpiece with a sensor attached to the tool holder. Specific acoustic emission frequency spectra and waveforms are emitted in the cutting zone for different tool wear states. Coating failures as well as other acoustic emission events due to workpiece damages can be robustly detected and distinguished by feature extraction and signal processing as well. The developed setup, the monitoring parameterisation techniques and signal processing algorithms as well as experimental and monitoring results are presented and discussed in this paper.

Damage Modes Recognition of Wood Based on Acoustic Emission Technique and Hilbert–Huang Transform Analysis

Identifying the different damage modes of wood is of great significance for monitoring the occurrence, development, and evolution of wood material damage. This paper presents the research results of the application of acoustic emission (AE) technology to analyze and evaluate the mapping relationship between the damage pattern of wood in the fracture process and the AE signal. For the three-point bending specimen with pre-crack, the double cantilever beam specimen, the single fiber tensile specimen, and the uniaxial compression specimen, the bending tensile compression test and the AE monitoring were performed, respectively. After the post-processing and analysis of the recorded AE signal, the results show that the peak frequency of AE is an effective parameter for identifying different damage modes of wood. In this study, the empirical mode decomposition (EMD) of the AE signal can separate and extract a variety of damage modes contained in the AE signal. The Hilbert–Huang transform (HHT) of the AE signal can clearly describe the frequency distribution of intrinsic mode function (IMF) components in different damage stages on the time scale, and can calculate instantaneous energy accurately, which provides a basis for damage mode recognition and lays a foundation for further accurate evaluation of the wood damage process.

Damage degree evaluation of masonry using optimized SVM-based acoustic emission monitoring and rate process theory

Evaluation of masonry damage is considerably challenging because of the non-homogeneous nature of masonry materials and the rapid attenuation of signals. To this end, a methodology combining qualitative analysis employing support vector machine-based acoustic emission (AE) monitoring and quantitative analysis using AE rate process theory was proposed. In this work, firstly, the propagation attenuation characteristics of the AE amplitude and frequency were studied using pencil-lead break tests. The hybrid PSO-SVM model was employed to automatically identify the damage stages of the AE signals based on the field in-situ axial compression test data. Then, laboratory compression and shear tests on small-scale specimens were performed with AE monitoring. The Boltzmann sigmoidal modified function between relative stress and AE hits was established. Based on qualitative and quantitative analysis, a damage index-based early warning range was proposed, which can help to decide the damage level of masonry structures. The results of this paper can provide some new insight for the analysis of masonry damage.

Acoustic Emission Features of Anthracite under the Influence of Loading Rate

Loading rate is an important impactor of the mechanical properties, as well as the deformation and failure mode of coal and rock. Using an RMT-301 rock mechanics tester and a Soft Island acoustic emitter, uniaxial compression and acoustic emission (AE) tests were carried out on coal samples under different loading rates. The results show that uniaxial compressive stress-strain curves of the rock samples each consist of four segments: compaction, elasticity, yield, and failure. As the loading rate increased from 0.01mm/s to 0.02mm/s, the peak strength rose, the post-peak deformability dropped, the brittle failure features of anthracite became more obvious, more AE events took place, and AE frequency increased. Energy analysis shows that, the faster the loading rate, the larger the AE count, the faster the energy accumulation, but the fewer the total energy accumulation.

Acoustic Emission Characterization of Transgranular Cracks in WC–Co Cemented Carbides During a One-way Scratch

The tribological behavior of tungsten carbide–cobalt materials is influenced by the cobalt content and the WC grains size. The main wear mechanisms in these materials are cobalt depletion, intergranular cracks, and WC grain cleavages. More specifically, coarse WC grains favor the apparition of transgranular cracks during sliding friction tests. A promising way to access in real-time blinded tribological contacts is the technique of acoustic emission (AE). This study clearly identifies transgranular cracks in AE signals. The AE energy and frequency of this mechanism were experimentally associated with the size of the transgranular cracks. A mechanical model based on the classical beam theory and harmonic motion equations confirms these relations. The AE centroid period (i.e., inverse of the centroid frequency) increases linearly with the size of the transgranular cracks. The AE energy increases linearly with the cube of the transgranular cracks length.

Acoustic signature identification of damage and wear mechanisms in a steel/glass sliding contact

The tribological behavior of a steel/glass ball-on-flat contact was studied by synchronizing the friction measurements with an acoustic emission device and a vision system. The results highlight two distinct friction regimes identified with low and high friction values. Their transition is characterized by a modification of acoustic emission signals. In addition, two main damage and wear mechanisms are identified: the creation and propagation of Hertzian cracks visible on the glass surface and the constitution of an interfacial layer of debris. The different accommodation mechanisms, activated successively or simultaneously, are identified for acoustic emission frequencies between 300 and 700 kHz. Eventually, this approach allows a real-time wear mechanisms identification and gives better insights about acoustic emission signals in relation to tribological systems.

Micro-cracking pattern recognition of hybrid CNTs/GNPs cement pastes under three-point bending loading using acoustic emission technique

The generation of microcracks has an important influence on the behaviour of concrete structures. In this study, the acoustic emission (AE) technique was used to investigate the fracture phenomena and micro-cracking behavior of hybrid carbon nanotubes (CNTs, the 1-D allotrope of carbon atoms) and graphene nanoplatelets (GNPs, 2D monolayer of sp2-hybridized carbon atoms), cement composites under three-point bending loading. In addition, dispersion level by ultraviolet visible spectroscopy (UV–Vis), mechanical performance, and micro-structure properties by scanning electron microscopy (SEM) of hybrid cement composites were studied. Different typical AE parameters like AE hits, rise time, frequency and amplitude distribution, and b-value were used for the AE analysis. The cracking pattern and failure mechanism of the specimens were observed into three stages: initial elastic stage, pre-fracture stage, and post-fracture stage. The conclusions were drawn as follows: (1) through the UV–Vis spectrometry, it is found that GNPs act as dispersion agents for CNTs; (2) hybrid specimens experienced large deformation with the increasing wt.% of CNTs + GNPs during post fracture phase, thus showing more ductile behavior as compared to the control sample; (3) based on the average frequency (AF) and RA value (rise time divided by the amplitude), the addition of CNTs + GNPs content in cement composites shifted the failure mode from shear to tensile; (4) the AE parameters are feasible to analyze and differentiate the cracking pattern between micro and macrocracks; (5) the field emission scanning electron microscopy (FESEM) image evidence that hybrid CNTs + GNPs particles are well dispersed within cement composites. Based on the obtained results, it was concluded that the acoustic emission technique is a favorable approach to monitor and identify the microcracking pattern in brittle concrete structures.

Deep learning approach to coal and gas outburst recognition employing modified AE and EMR signal from empirical mode decomposition and time-frequency analysis

Acoustic emission (AE) and electromagnetic radiation (EMR) monitoring technology have been widely used in coal and rock dynamic disaster monitoring. However, the prediction of coal and gas outburst using original signal directly may cause the false and missing alarms due to the failure to identify valuable potential information, which may lead to the decrease of prediction accuracy. This paper presents a new method to identify the precursor features of coal and gas outburst using two-dimensional Convolutional Neural Network (CNN). Empirical mode decomposition (EMD) was performed for AE or EMR signals, and the Intrinsic Mode Functions (IMFs) with Variance Contribution Rate (VCR) greater than 90% were merged into modified signal. Since the frequency of AE or EMR signal can change with different coal and rock fracture stages, one-dimensional modified signal was transformed into a two-dimensional time-frequency graph using the short-time Fourier transform (STFT). The time-frequency graph was input into CNN that recognizes the time-frequency characteristics of AE or EMR using SoftMax regression at the end of the network. Compared with the original signal, the precursor characteristics of modified AE or EMR signals can be better learned by CNN. The proposed method was applied to the Jinjia Coal Mine database which was identified as having outburst risk, and it was found that the recognition accuracy was better than other approaches. The proposed method achieves maximum recognition accuracy of 98.00% (AE), and 97.20% (EMR) in Jinjia Coal Mine database, respectively.

Investigation on Acoustic Emission Kaiser Effect and Frequency Spectrum Characteristics of Rock Joints Subjected to Multilevel Cyclic Shear Loads

Rock joints have obvious acoustic emission (AE) Kaiser effect and Felicity effect under multilevel cyclic shear conditions. The TFD-20H/50J rock shear apparatus was used to carry out cyclic loading and unloading joint shear tests, and the acoustic emission parameters and frequency spectrum characteristics of the whole shearing process were analyzed. The results show that, under the cyclic loading, the shear stress-displacement curve forms several cyclic hysteresis loops, and the number of loops increases with the increase of normal stress. With the cycles increase, the shear damage gradually increases, and the Felicity ratio gradually decreases. The Felicity ratio at the final shear failure moment is about 0.94~0.99. The ratio of the RA value (rise time/amplitude) and the average frequency value (RA-AF) is used to classify the cracking mode of the joint sample. There are two AE crack signal types (tensile type and shear type) during shear damage. The peak frequency is displayed as high, medium, and low three frequency bands, which are distributed in the range of 0~35 kHz, 35~122 kHz, and 122~300 kHz, respectively. Both low-frequency and high-frequency signals account for less than 10%, and medium-frequency signals account for more than 90%. The research of the AE monitoring signals of multilevel shear behaviors can help understand the shear-friction mechanisms of rock joints.

Characterisation of hydrogen assisted cracking in modified 9Cr-1Mo steel welds using acoustic emission non destructive technique

ABSTRACT This study aims a systematic experimental investigation using acoustic emission (AE) non-destructive technique for online monitoring of hydrogen assisted cracking (HAC) in modified 9Cr-1Mo steel (P91 steel) welds during Gap-Bead on Plate (G-BOP) and implant tests. Welds made without preheating, with different preheating and combined pre and post heating were tested using G-BOP test. AE results of G-BOP tests have shown that time duration in which HAC active varies with temperature of preheating and combined pre and post heating. Reduction of AE activity in welds made with pre and post heating compared to those only made with preheating revealed a beneficial effect of the former in reducing HAC. In the case of implant tests, crack initiation and propagation are identified for different applied loads from the AE analysis. An attempt has been made to compare HAC during G-BOP tests and implant tests using AE frequency analysis. The dominant frequency of AE signals characteristic of HAC has been identified. This study shows the potential of using AE frequency analysis for online monitoring of hydrogen assisted cracking (HAC) in welds.

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.

The Use of Acoustic Emission and Neural Network in the Study of Phase Transformation below MS.

Acoustic emission and dilatometry were applied to investigate the characteristics of phase transformations in bearing steel 100CrMnSi6-4 during austempering below the martensite start temperature (MS 175 °C) at 150 °C. The aim of this study is to characterize the product of transformation occurring below the MS temperature using various research methods. Analysis of the dilatometric curves shows that, after the formation of athermal martensite below the MS temperature, the austenite continues to undergo isothermal transformation, indicating the formation of bainite. Additionally, tests were carried out with the use of acoustic emission during isothermal hardening of the adopted steel. The obtained acoustic emission signals were analyzed using an artificial neural network. The results, in the form of a graph of the frequency of acoustic emission (AE) event occurrence as a function of time, make it possible to infer about the bainite isothermal transformation. The results of this research may be used in the future to design optimal heat treatment methods and, consequently, may enable desired microstructure shaping.

Effect of propagation distance on acoustic emission of carbon fiber/epoxy composites

This study examined the change in acoustic emission as a function of measurement position of fiber-reinforced composites. Single-edge-notched carbon fiber/epoxy composites were prepared and tested under cyclic loading, with sensors located at specific distances from the end of the notch. Although the Ib-value increased overall, the degree of increase significantly varied with position and acoustic emission frequency. Notably, the proportion of acoustic emission signals for each failure mode varied due to a high attenuation rate at high frequencies, which increased the Ib-value. Accordingly, the high-frequency fiber-failure signals significantly affected the Ib-value. This study focused on the importance of analyzing acoustic emission signals by considering the crack location and frequency-dependent attenuation rate. We concluded that an acoustic emission sensor should be located 20–40 mm from the crack location for woven carbon fiber/epoxy composites.

Diamond wheel grinding characteristics of 3D orthogonal quartz fiber reinforced silica ceramic matrix composite

Fiber-reinforced silica ceramic matrix composites (SiO2f/SiO2) have gained extensive attention in recent years for its applications in aeronautics field such as radar radome and window. However, the machining properties and mechanism of the material remain unclear. The features and mechanical properties of the material itself have a significant influence on both its machining characteristics and surface integrity. Thus, a full-factor grinding experiment is conducted using a 3D orthogonal SiO2f/SiO2 aiming to obtain its machining characteristics. The effects of grinding parameters and tools on the grinding force, surface roughness, and material damage type are investigated using a dynamometer, Scanning Electron Microscope (SEM), and Acoustic Emission (AE) analysis. The AE frequency band is analyzed, and a semi-analytical force model is established to study the difference between a single grain and wheel grinding. It was found that the changes in surface roughness correlate with the changes in grinding force, with fiber fracture being the main reason behind the increase in grinding force. Finally, the material removal mechanism was studied based on the AE analysis. It was found that the removal mechanism is fiber fracture dominated with matrix crack and debonding, and the primary sources of energy consumption are fiber fracture and friction.