Acoustic Emission Power Research Articles

Measurement and Simulation of the Propagation of Impulsive Acoustic Emission Sources in Pipes

Abstract: Acoustic Emission (AE) testing is a non-destructive evaluation technique that has gained significant attention in pipeline monitoring. Pencil-lead breaks (PLBs) are commonly used in reproducing and characterising sensors used in AE applications and have emerged as a valuable tool for calibration processes. This technique involves breaking a pencil lead by pressing it on the surface of the test structure and applying a bending moment at a given angle on a surface. The applied force produces a local deformation on the test surface, which is released when the lead breaks. The fracture in these PLBs is assumed to be a step unload; however, this is not the case. In this work, a series of PLB source experiments complemented with parallel numerical simulations were carried out to investigate the actual unload rate by correlating the relationship between AE speed, frequency, and power from PLBs. This was achieved by varying the simulation unload rates recorded over a duration of 2 s on a steel pipe and comparing to the experiment. Analysis of the investigated results from the experimental and numerical models suggests that although the AE line structure of a PLB can be reproduced by simulation for short times only (1 µs), the actual unload rate for PLBs is in the region of 10–8 s. It is concluded that FEA has the potential to help in the recovery of the temporal structure from real AE structures. The establishment of this model will provide a theoretical basis for future studies on the monitoring of non-impulsive AE sources such as impact on pipelines using finite element analysis.

Application of Artificial Neural Network for Internal Combustion Engines

In this research, acoustic emission (AE) technology is used to detect faults in the valves in the internal combustion engine, where the cylinder head of a spark ignition engine was used as an experimental setup. The study was conducted on three types of valve damage ((clearance, half-notch, and notch) on valve leakage. The study proved that the acoustic emission technique is an effective method in detecting damage to valves in both the time and frequency domain. The neural network was trained based on time domain analysis using AE parametric features (, number, absolute AE power, maximum signal amplitude, and average signal level).

(Digital Presentation) Lifetime Prediction of SiC Power Module By Using Time-Series Analysis of Acoustic Emission during Power Cycling Test

Acoustic emission (AE) based condition monitoring is a popular method to inspect a material health condition in many areas[1]. It monitors irreversible structure changes of material through radiation of acoustic waves. Typically, the structure change generates a typical spectrum of acoustic waves starting at 1 kHz, and falling off at several MHz, which can be detected and analyzed via an AE system. Meanwhile, AE signals convey massive information such as rise time, AE energy, AE peak frequency, which are expressive of the failure process. AE based condition monitoring technology exhibits excellent capability of detecting, positioning, and characterizing damage and has been wildly used in monitoring of inner structure changes in bridges, pressure containers, and pipeline systems. In the last decade, the AE technology has been applied to condition monitoring of power modules and shows a promising application scenario in the power electronics field, which help us acquire accurate failure information and making precise perdition of a failure in the power module[2].In this work, we firstly combined the AE monitoring and a deep learning model - Gated Recurrent Unit (GRU) to monitor and predict the failure of power module as shown in Figure 1[3]. The proposed method integrally analyzes and summarizes the patterns from the time-series data obtained during power cycle tests of power modules and predicts the failures. Specifically, the time-series data consisting of junction temperature, electric power, thermal resistance, and several AE parameters were obtained from AE sensor data and power cycling test. The proposed method (a) automatically extracts common or different time-series patterns among devices and estimates device states, and (b) effectively predicts failures by switching prediction models according to changes in device states.By time-series analysis of AE sensor data, it was confirmed that the AE parameters obtained from the AE sensor data capture the progression and signs of failures. In addition, by verifying the effectiveness of the time-series pattern detection, it was confirmed that the proposed method can accurately extract the timeseries patterns during normal conditions, the time-series patterns that show signs of failure, and the time-series patterns after failure. Furthermore, by verifying the prediction accuracy and learning efficiency, it was showed that the proposed method is capable of faster learning while retaining the same or better performance compared to existing methods. Reference [1] A. Nair and C. Cai, "Acoustic emission monitoring of bridges: Review and case studies," Eng Struct, vol. 32, no. 6, pp. 1704-1714, 2010.[2] S. Müller, C. Drechsler, U. Heinkel, and C. Herold, "Acoustic emission for state-of-health determination in power modules," in 2016 13th International Multi-Conference on Systems, Signals & Devices (SSD), 2016: IEEE, pp. 468-471.[3] R. Dey and F. M. Salem, "Gate-variants of gated recurrent unit (GRU) neural networks," in 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS), 2017: IEEE, pp. 1597-1600.

Phenomenological approach towards modelling the acoustic emission due to plastic deformation in metals

Considering acoustic emission (AE) as a phenomenon reflecting the elastic energy dissipation process in deforming metals, a simple yet self-consistent model is proposed to account for the AE behaviour accompanying microstructure evolution during uniform plastic deformation of metals. The relationship between the AE power, mobile dislocation density and plastic flow characteristic parameters - the strain hardening rate and flow stress - is derived and experimentally verified for face centred cubic metals with different stacking fault energies (SFE). Despite its simplicity, the proposed purely phenomenological relation captures most of the salient features of the AE behaviour of early deformation stages in metals.

Study on the acoustic emission effect of plasma plume in pulsed laser welding

Abstract The acoustic emission (AE) signals were detected in real-time during the process of pulsed YAG laser welding. Some characteristic analysis methods, including AE count statistic, root mean square (RMS) waveform calculation and power spectrum distribution, were used to analyze the AE signals. According to the AE signals, the characteristics of plasma plume and penetration of weld bead were studied. The results showed that the source mechanism for AE generation was the recoil force and thermal vibration generated by the plasma plume acting on molten pool. The AE signals detected in welding contained a lot of valuable information about plasma plume. AE count, RMS waveform and power spectrum of AE signals showed the energy characteristics of plasma plume in time and frequency domain. Therefore, AE signals detected in pulsed laser welding had clear correlations with plasma plume, which were valuable to evaluate the plasma plume and the penetration of weld bead.

Анизотропия сигнала акустической эмиссии при царапании монокристалла алюминия

In modern materials science, one of the main directions of solving the problem of improving the performance of parts and mechanisms is a radical improvement in the properties of material surface through its modification or application of coatings. To control the mechanical properties of structurally modified layers and coatings, local test methods are becoming increasingly common. Indentation and scratch testing are the most popular among such local techniques. To increase the significance of these tests, it is plausible to combine them with the registration of the acoustic emission (AE) signal. The purpose of this work is to assess the sensitivity of the AE parameters to the change in the dislocation slip systems using an aluminum single crystal as a test piece. The sample surface has been polished and analyzed by scanning electron microscopy and EBSD. The localized deformation test has been carried out on an instrumented tribometer by moving a conical indenter around the circle with a radius of 100 μm under a constant load of 2 N. The advantage of such a test is its ability to plastically deform the surface continuously in all crystallographic directions, preserve the stationary conditions for external factors and ensure the optimal signal-to-noise ratio for recording the AE signal. It is established that, depending on the direction of scratching, the hardness and the AE power exhibit opposite trends, both following the anisotropic properties of a cubic crystal lattice, while changing the configuration of active slip systems can be monitored using an interferometer according to characteristic traces of the slip lines on the polished surface of the sample near еру trace indenter. It is shown that the direction of scratching affects not only the energy of the AE signal, but also its spectral characteristics, in particular, the median frequency, and the extremes in the AE power diagram do not coincide with the extremes in the median frequency diagram.

Vibration and acoustic emission monitoring the stability of peakless tool turning: Experiment and modeling

Acoustic emission (AE) study of steady and chatter mode peakless tool turning has been carried out in order to reveal an acoustic emission response to the workpiece chatter during fine turning. Molecular dynamics simulation of acoustic emission response to chatter was used to find out fundamental system characteristics. Experimental dependencies of AE signal amplitude, median frequency and power spectrum have been obtained and compared to those obtained from the molecular dynamics (MD) simulation. Both experimental and MD simulated AE signal spectral characteristics proved to be sensitive to chatter mode vibrations. Median frequency showed a drop in chatter mode cutting as well as power spectrum shifted to the low frequency range. Such a relationship has been attributed to the growing level of the system potential energy.

Detection and characterization of stainless steel SCC by the analysis of crack related acoustic emission

In the paper the results of the acoustic emission (AE) based detection and characterization of stress-corrosion cracking (SCC) in stainless steel are presented. As supportive methods for AE interpretation, electrochemical noise, specimen elongation measurements, and digital imaging of the specimen surface were used. Based on the defined qualitative and quantitative time and power spectra characteristics of the AE bursts, a manual and an automatic procedure for the detection of crack related AE bursts were introduced. The results of the analysis of the crack related AE bursts indicate that the AE method is capable of detecting large scale cracks, where, apart from intergranular crack propagation, also some small ductile fractures occur. The sizes of the corresponding ductile fracture areas can be estimated based on a relative comparison of the energies of the detected AE bursts. It has also been shown that AE burst time and power spectra features can be successfully used for the automatic detection of SCC.

A real-time approach to acoustic emission clustering

A common target of clustering in acoustic emission (AE) non-destructive inspection technique (NDT) is to distinguish between the sources of different origin and to get a deeper insight into the interrelation between the underlying processes such as plastic deformation, crack initiation, corrosion cracking, etc. The major drawback of the most popular conventional schemes such as k-means and fuzzy c-means is that they are iterative in nature, which hinders their real-time applications. Inspired by the sequential k-means procedure, i.e. a non-iterative variant of the classic k-means, we present a novel classification technique designed for real-time applications. The proposed approach is “non-supervised”, i.e., both the number of clusters and their elements are inferred from the data distribution in a multi-dimensional metric space. In its present form the approach is capable to adopt various dissimilarity measures to compare AE power spectral densities. A series of tests on different probing datasets has been performed to prove the efficiency of the proposed approach.

Cluster Analysis of Acoustic Emissions Measured during Deformation of Duplex Stainless Steels

Aiming at better understanding of the deformation mechanisms involved into plastic deformation of duplex stainless steels, acoustic emission (AE) measurements were performed during room temperature tensile deformation of model steels and alloys with different phase structure including single phase i Fe­30Cr and £ SUS316L steels and duplex i­£ SUS329J4L, Fe­24Cr­6Ni alloys. The real time AE investigations were complemented by microscopic investigations of deformed microstructures. The quantitative AE analysis revealed different AE patterns which were correlated with underlying deformation mechanisms and microstructural transformations. The cluster analysis of AE power spectral densities has been proven effective in revealing specific groups of signals (clusters) which appear on different deformation stages in different steel variants, which can be correlated with involved source mechanisms of plastic deformation and microstructural processes including dislocation slip in either fcc or bcc lattice of £ or i phase, respectively, twinning in the SUS 316L steel and £­iA martensitic transformation in the Fe­24Cr­6Ni model alloy. [doi:10.2320/matertrans.MBW201203]

Revealing, identifying and assessing “non‐predictable flaws”, crack type IV, by quantitative acoustic emission non‐destructive inspection technology, photo‐elastic and metallurgical methods

AbstractQAE NDI technology has been adapted to revealing, identifying and assessing one type of previously non‐predictable failure of high energy equipment and piping, known as crack type IV. The research established the following: Cracks of type IV correspond to transverse shear crack mode II, according to the classification used in fracture mechanics and the physics of solids. The length of crack mode II jumps, the velocity of it propagation are significantly higher than in case crack mode I under the same loads. The earliest quantitative statistical Acoustic Emission (AE) indications and peculiarities necessary and sufficient to reveal cracks in specimens loaded by tension or shear loads, especially the differences in the AE power, median Energy, Average Frequency and Hit Rate of the AE signals flow. etc. Based on the findings described above have been formulated requirements and technological solutions for revealing, typifying and assessing crack IV.

Study of the Power Spectrum of Acoustic Emission (AE) by Accelerometers in Fluidized Beds

Power spectrums of acoustic emission (AE) on the walls of fluidized beds were calculated, to investigate the particle movement in the bed. It is determined that the main frequency of the AE power s...

Identification of coating damage processes in corroded galvanized steel by acoustic emission wavelet analysis

Scratch tests using Acoustic Emission (AE) were performed on corroded hot dip galvanized samples. Coatings and scratches were studied by scanning electron microscopy and electron dispersive X-ray spectrometry, and the coatings additionally by X-ray diffraction. Damage mechanisms could be identified applying Wavelet Transform analysis to AE signals. The evolution of specific AE wavelet parameters like the wavelet power was suited to identify damage mechanisms. In general, the AE power was distributed in certain scale (frequency) bands in the time-scale plane, and different coating damage mechanisms could be assigned to different bands. In particular, signals due to corrosion and non-corrosion mechanisms could be neatly separated.

Online monitoring of acoustic emission for quality control in drilling of polymeric composites

Automation and optimization of the manufacturing processes play an important role in enhancing productivity. For this, monitoring and diagnostic systems are becoming increasingly necessary to asses the response of materials in manufacturing. In this paper, acoustic emission (AE) sensing was employed for on-line detection of workpiece status and to improve the process stability and workpiece quality by minimising associated defects. Drilling trials were conducted on woven glass fabric/epoxy with high speed steel (HSS) drills to determine the relationship between AE rms and cutting parameters. The variation of AE rms and power are in close correlation to the flank wear and hole shrinkage. The experimental results show that AE is very sensitive to the response of the drilling environment.

In Situ Monitoring of Hydrogen Storage Alloy Negative Electrode during Charging by an Acoustic Emission Technique

We have monitored the physical processes linked to electrochemical processes in situ occurring at MmNi 3.6 Mn 0.4 Al 0.3 Co 0.7 alloy negative electrodes using an acoustic emission (AE) technique in terms of waveforms, power spectra, and time history of the AE signals. Hydrogen evolution was characterized by an AE waveform with a long duration of over 0.15 ms and a power spectrum with a relatively narrow frequency distribution of around 0.6 MHz and a maximum amplitude at about 0.1 MHz. In contrast, cracking of the alloy particles was characterized by a burst-type AE waveform with duration shorter than 0.1 ms and large amplitude and a power spectrum with wide frequency distribution, with some peaks at frequencies more than 0.6 MHz. From the analyses of the AE waveforms, power spectra, and time history during the first charging process for the alloy negative electrode, it was inferred that cracking occurs due to lattice expansion by hydrogen absorption occurring intensively in the first half of the charging process.

Acoustic Emission as a Probe of the Kinetics of the Martensitic Transformation in a Shape Memory Alloy

The kinetics of the martensitic transformation in a CuAlMn shape memory alloy (SMA) has been studied using the acoustic emission (AE) technique. It is demonstrated that the volume fraction of martensite as a function of time and temperature can be derived from the measured AE power. The fraction data obtained can be described by the Koistinen and Marburger (Acta Metall. 7 (1959) 59) equation with high accuracy, which indicates that the nucleation of martensite takes place heterogeneously and that the average volume of martensite crystals is constant over the extent of the transformation. The martensite-start temperature determined from the measured AE data is in good agreement with the value found by differential scanning calorimetry (DSC). Furthermore, the results of AE experiments on the SMA are compared with optical Confocal Laser Scanning Microscopy (CLSM) observations of the surface of the SMA. The observations show that both small and large martensite plates are formed both at the beginning and at the end of the transformation, which is in agreement with the assumption of a constant average volume of martensite crystals used in the Koistinen and Marburger model.

Kinetics of the martensitic transformation in low-alloy steel studied by means of acoustic emission

The kinetics of the martensitic transformation in three carbon steels (C60, C70 and C80) have been studied using the acoustic emission (AE) technique. It is demonstrated that the volume fraction of martensite f as a function of time t during cooling can be derived from the measured AE power ℧ 2 , since ℧ 2 ∝ df /dt . An analysis of results shows that the fraction data as a function of temperature T can be described by the Koistinen and Marburger (KM) equation with high accuracy. This indicates that the nucleation of martensite takes place heterogeneously and that the average volume of martensite crystals is constant over the extent of the transformation. The change in kinetics with carbon content is attributed to the amount of dislocations created in the neighboring austenite, which influences the degree of autocatalysis. Also, the acoustic emission energy generated per unit volume of martensite has been found to scale with the change in dislocation density in the formed martensite as the carbon content is varied.

A study of acoustic emission energy generated during bainite and martensite formation

Acoustic emission (AE) signals generated during bainite and martensite formation in steel C45 have been measured, and the AE energy has been correlated with the strain energy accompanying both displacive transformations. The gas tungsten arc welding process was used to vary the volume transformation rates of bainite and martensite formation. The root mean square (rms) voltage Urms of the continuous AE signals was measured during travelling arc welding and after spot welding. Depending on the cooling rate and the mean austenite grain size, martensite or bainite is formed in the weld. After spot welding with moderate arc currents, only martensite was formed during cooling, which was reflected by a peak in the Urms data: the martensite peak. An analysis of the results shows that the AE energy produced during the transformation (∫Ū2m dt) is proportional to the volume Vm of martensite in the spot weld, with proportionality factor km. During travelling arc welding, bainite and martensite formation occur simultaneously and both displacive transformations contribute to the measured AE power at each moment. The AE power due to bainite formation (Ū2b) was calculated using the obtained proportionality factor km and was found to be proportional to the volume rate of bainite formation dVb/dt with proportionality factor kb.

Effect of solid solution hardening and stacking fault energy on plastic flow and acoustic emission in Cu–Ge alloys

The acoustic emission (AE) has been investigated systematically in tensile strained model Cu–Ge alloys with germanium concentration varied from 0.1 to 9.0 at.%. The role of various micro-structural factors in AE is discussed and the effects of solid solution hardening and stacking fault energy (SFE) on the AE power spectra are clarified. It is shown that even a small (0.1 at.%) addition of Ge changes AE significantly, whereas the dislocation AE mechanisms are similar in pure copper and its dilute alloys. It is argued that the solution hardening is a main factor governing the AE behaviour in dilute Cu–Ge solutions, resulting in the increase of lattice friction, decrease of dislocation velocity and, consequently, in reduction of the AE energy. The AE technique shows that twinning occurs from the very early stage of plastic deformation together with dislocation slip in copper alloys with germanium content as high as 5.7 at.% and the SFE is of 20 mJ m −2 and lower. Both types of AE sources—dislocation glide and twinning—have distinct features in the AE power spectra and waveforms and are discriminated with a help of data categorization technique and cluster analysis of AE.

Spectral analysis of acoustic emission during cyclic deformation of copper single crystals

Abstract Acoustic emission (AE) is investigated during fatigue at constant-plasticstrain control of copper single crystals oriented for easy glide, aiming at AE monitoring of structural transformation and dislocation self-organization during cyclic deformation. The results of AE analysis are compared with the cyclic response and hysteresis loop shape on different stages of fatigue. It is shown that the modern AE technique employing spectral analysis is sensitive to all essential structural changes in dislocation ensembles during fatigue such as matrix hardening, formation of veins and channels, persistent slip band (PSB) nucleation and development until crack initiation. The evidence is given for early identification of strain localization by means of AE. It is shown that the maximum of median frequency of the AE power spectral density at the intermediate stage of rapid hardening can be considered as an indicator of local softening followed by strain localization in PSBs. The moment of crack nucleation is...