scholarly journals Analytical Modeling of Acoustic Emission Signals in Thin-Walled Objects

2019 ◽  
Vol 10 (1) ◽  
pp. 279 ◽  
Author(s):  
Vera Barat ◽  
Denis Terentyev ◽  
Vladimir Bardakov ◽  
Sergey Elizarov
Keyword(s):  
Acoustic Emission ◽  
Data Processing ◽  
Defect Detection ◽  
Analytical Method ◽  
Analytical Modeling ◽  
Location Accuracy ◽  
Defect Location ◽  
Thin Walled ◽  
Noisy Background ◽  
Ae Signals

For the effective detection of acoustic emission (AE) impulses against a noisy background, the correct assessment of AE parameters, and an increase in defect location accuracy during data processing are needed. For these goals, it is necessary to consider the waveform of the AE impulse. The results of numerous studies have shown that the waveforms of AE impulses mainly depend on the properties of the waveguide, the path along which the signal propagates from the source to the sensor. In this paper, the analytical method for modeling of AE signals is considered. This model allows one to obtain model signals that have the same spectrum and waveform as real signals. Based on the obtained results, the attenuation parameters of the AE waves for various characteristics of the waveguide are obtained and the probability of defect detection at various distances between the AE source and sensor utilized for evaluation.

ANALYTICAL MODELING OF ACOUSTIC EMISSION SIGNALS IN THIN-WALLED OBJECTS

2020 ◽  
pp. 23-29
Author(s):  
V. A. Barat ◽  
D. A. Terentyev ◽  
V. V. Bardakov ◽  
S. V. Elizarov
Keyword(s):  
Acoustic Emission ◽  
Data Processing ◽  
Analytical Method ◽  
Analytical Modeling ◽  
Location Accuracy ◽  
Noise Background ◽  
Defect Location ◽  
Thin Walled ◽  
Ae Signals

For the effective detection of acoustic emission (AE) impulses against the noise background, the correct assessment of AE parameters and the increase in the defect location accuracy during data processing, it is necessary to consider the waveform of the AE impulse. The results of numerous studies have shown that the waveform of the AE impulse mainly depends on the properties of the waveguide – the path along which the signal propagates from the source to the sensor. In this paper, the effective analytical method for modeling of AE signals is considered, which allows to obtain model signals that have the shape of the envelope and spectrum as real one. Based on the obtained results, the attenuation parameters of AE waves for various characteristics of the waveguide and evaluate the probability of defects detecting at various distances between the AE source and sensor are evaluated. Based on the results obtained, the parameters of the AE impulse for various characteristics of the waveguide were estimated, and the parameters of the AE pulses and noise were compared.

Empirical Approach to Defect Detection Probability by Acoustic Emission Testing

2021 ◽  
Vol 11 (20) ◽  
pp. 9429
Author(s):  
Vera Barat ◽  
Artem Marchenkov ◽  
Valery Ivanov ◽  
Vladimir Bardakov ◽  
Sergey Elizarov ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Defect Detection ◽  
Probabilistic Models ◽  
Fatigue Cracks ◽  
A Priori ◽  
Empirical Approach ◽  
Empirical Generalization ◽  
Microstructure Parameters ◽  
A Priori Estimation ◽  
Ae Signals

Estimation of probability of defect detection (POD) is one of the most important problems in acoustic emission (AE) testing. It is caused by the influence of the material microstructure parameters on the diagnostic data, variability of noises, the ambiguous assessment of the materials emissivity, and other factors, which hamper modeling the AE data, as well as the a priori determination of the diagnostic parameters necessary for calculating POD. In this study, we propose an empirical approach based on the generalization of the experimental AE data acquired under mechanical testing of samples to a priori estimation of the AE signals emitted by the defect. We have studied the samples of common industrial steels 09G2S (similar to steel ANSI A 516-55) and 45 (similar to steel 1045) with fatigue cracks grown in laboratory conditions during cyclic testing. Empirical generalization of data using probabilistic models enables estimating the conditional probability of record emissivity and amplitudes of AE signals. This approach allows to eliminate the existing methodological gap and to build a comprehensive method for assessing the probability of fatigue cracks detection by the AE testing.

scholarly journals Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding

2021 ◽  
Vol 11 (15) ◽  
pp. 7045
Author(s):  
Ming-Chyuan Lu ◽  
Shean-Juinn Chiou ◽  
Bo-Si Kuo ◽  
Ming-Zong Chen
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Frequency Domain ◽  
Monitoring System ◽  
Welding Quality ◽  
Steel Sheets ◽  
Signal Features ◽  
Ae Signal ◽  
Laser Microwelding ◽  
Ae Signals

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.

scholarly journals Acoustic Emission Testing and Ib-Value Analysis of Ultraviolet Light-Irradiated Fiber Composites

2021 ◽  
Vol 11 (14) ◽  
pp. 6550
Author(s):  
Doyun Jung ◽  
Wonjin Na
Keyword(s):  
Acoustic Emission ◽  
Irradiation Time ◽  
Tensile Load ◽  
Failure Behavior ◽  
Value Analysis ◽  
Emission Testing ◽  
Final Failure ◽  
Acoustic Emission Testing ◽  
Woven Glass Fiber ◽  
Ae Signals

The failure behavior of composites under ultraviolet (UV) irradiation was investigated by acoustic emission (AE) testing and Ib-value analysis. AE signals were acquired from woven glass fiber/epoxy specimens tested under tensile load. Cracks initiated earlier in UV-irradiated specimens, with a higher crack growth rate in comparison to the pristine specimen. In the UV-degraded specimen, a serrated fracture surface appeared due to surface hardening and damaged interfaces. All specimens displayed a linearly decreasing trend in Ib-values with an increasing irradiation time, reaching the same value at final failure even when the starting values were different.

Acoustic Emission Monitoring of Leaks in Pipes for Transport of Liquid and Gaseous Media: A Model Experiment

2006 ◽  
Vol 13-14 ◽  
pp. 351-356 ◽  
Author(s):  
Andreas J. Brunner ◽  
Michel Barbezat
Keyword(s):  
Acoustic Emission ◽  
Model Experiment ◽  
Structural Integrity ◽  
Fiber Composite ◽  
Leak Testing ◽  
Pipe Segment ◽  
Potential Applications ◽  
Gaseous Media ◽  
The Media ◽  
Ae Signals

In order to explore potential applications for Active Fiber Composite (AFC) elements made from piezoelectric fibers for structural integrity monitoring, a model experiment for leak testing on pipe segments has been designed. A pipe segment made of aluminum with a diameter of 60 mm has been operated with gaseous (compressed air) and liquid media (water) for a range of operating pressures (between about 5 and 8 bar). Artificial leaks of various sizes (diameter) have been introduced. In the preliminary experiments presented here, commercial Acoustic Emission (AE) sensors have been used instead of the AFC elements. AE sensors mounted on waveguides in three different locations have monitored the flow of the media with and without leaks. AE signals and AE waveforms have been recorded and analysed for media flow with pressures ranging from about 5 to about 8 bar. The experiments to date show distinct differences in the FFT spectra depending on whether a leak is present or not.

scholarly journals A Principal Component Analysis of Acoustic Emission Signals from a Landing Gear Component

2008 ◽  
Vol 13-14 ◽  
pp. 41-47 ◽  
Author(s):  
Rhys Pullin ◽  
Mark J. Eaton ◽  
James J. Hensman ◽  
Karen M. Holford ◽  
Keith Worden ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Acoustic Emission ◽  
Background Noise ◽  
Principal Component ◽  
Component Analysis ◽  
Landing Gear ◽  
Fracture Detection ◽  
Artificial Fracture ◽  
High Level ◽  
Ae Signals

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

Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

2007 ◽  
Vol 329 ◽  
pp. 15-20 ◽  
Author(s):  
Xun Chen ◽  
James Griffin
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Material Removal ◽  
Grinding Process ◽  
Short Time Fourier Transform ◽  
Fundamental Investigation ◽  
Mechanical Behaviours ◽  
Wavelets Transform ◽  
Short Time ◽  
Ae Signals

The material removal in grinding involves rubbing, ploughing and cutting. For grinding process monitoring, it is important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with single grit cutting tests. Acoustic Emission (AE) signals would give the information relating to the groove profile in terms of material removal and deformation. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and time constant measurements provided the principle components for classifying the different grinding phenomena. Identification of different grinding phenomena was achieved from the principle components being trained and tested against a Neural Network (NN) representation.

Schadenserfassung auf Radsatzlaufflächen/Defect detection on wheelset treads – Measurement of Magnetic Barkhausen Noise to detect varying workpiece conditions

2019 ◽  
Vol 109 (11-12) ◽  
pp. 811-815
Author(s):  
B. Denkena ◽  
B. Bergmann ◽  
H. Blech
Keyword(s):  
Acoustic Emission ◽  
Process Parameters ◽  
Defect Detection ◽  
Material Properties ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Optimal Process ◽  
Process Reliability

Unterschiedliche Belastungshistorien von Eisenbahnrädern führen zu Werkstoffveränderungen in der Lauffläche. Diese verursachen sporadisches Werkzeugversagen und verringern so die Prozesssicherheit. Die Messung der Material- und Prozesseigenschaften mit Barkhausenrauschen und Körperschall erlauben, individuelle Bearbeitungsparameter für jedes Exemplar festzulegen. Gezeigt werden die Herausforderungen in der Radsatzbearbeitung, und welche Informationen sich durch die Messtechniken gewinnen lassen.   Different load histories of train wheels lead to high variance of material properties on the running tread. Those cause unpredictable tool break and reduce process reliability. The measurement of magnetic Barkhausen noise and acoustic emission allow to gain information of the workpiece and the running process, to find optimal process parameters for the reconditioning of every individual wheel. Typical issues in train wheel machining and results of measurements are presented.

Experiment on Surface Roughness and Acoustic Emission Signal in Infeed Period of Cylindrical Plunge Grinding

2013 ◽  
Vol 690-693 ◽  
pp. 2442-2445 ◽  
Author(s):  
Hao Lin Li ◽  
Hao Yang Cao ◽  
Chen Jiang
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Feed Rate ◽  
Grinding Process ◽  
Experiment Research ◽  
Plunge Grinding ◽  
Emission Signal ◽  
Ae Signal ◽  
Ae Signals

This work presents an experiment research on Acoustic emission (AE) signal and the surface roughness of cylindrical plunge grinding with the different infeed time. The changed infeed time of grinding process is researched as an important parameter to compare AE signals and surface roughnesses with the different infeed time in the grinding process. The experiment results show the AE signal is increased by the increased feed rate. In the infeed period of the grinding process, the surface roughness is increased at first, and then is decreased.

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