Towards the effect of acoustic emission (AE) sensor positioning within AE signal parameters in sliding on bulk ultrafine-grained materials

2017 ◽  
Author(s):  
A. V. Filippov ◽  
S. Yu. Tarasov ◽  
O. A. Podgornykh ◽  
P. A. Chazov ◽  
N. N. Shamarin ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Signal Parameters ◽  
Ultrafine Grained ◽  
Ae Signal ◽  
Ultrafine Grained Materials ◽  
Sensor Positioning

Surface Roughness Prediction Based on Acoustic Emission Signals in High-Precision Diamond Turning of Rapidly Solidified Optical Aluminum Grade (RSA443)

2020 ◽  
Vol 841 ◽  
pp. 363-368
Author(s):  
Zvikomborero Hweju ◽  
Khaled Abou-El-Hossein
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Prediction Accuracy ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Surface Roughness Prediction ◽  
Peak Rate ◽  
Signal Parameters ◽  
Roughness Prediction ◽  
Ae Signal

Acoustic emission signal-based prediction of surface roughness has been utilized widely, yet little work has been done in this regard on RSA443. This paper seeks to study the correlation between acoustic emission (AE) signal parameters and surface roughness. Estimation of surface roughness using AE signal parameters and subsequent examination of the influence of AE signal parameters (root mean square, peak rate and prominent frequency) on the accuracy of the RSM model in surface roughness prediction are carried out. The experiment is designed using the Taguchi L9 orthogonal array to minimize the number of experiments. Emitted acoustic signals are captured using a Piezotron sensor. Three RSM models are formulated and compared in this study: a model that uses only critical machining parameters (cutting speed, depth of cut and feed rate), a model that uses only AE signal parameters (root mean square, peak rate and prominent frequency) and a model that uses both critical machining parameters and AE signal parameters. An assessment based on the models’ mean absolute percentage error (MAPE) is made to see if AE signal parameters have any contribution towards surface roughness prediction accuracy. The order of parameter significance in the most accurate model is investigated in this paper. The mean absolute percentage error results for the models indicate that the model in which AE signal parameters are utilized in conjunction with critical machining parameters has the highest prediction accuracy of 97.32%. The model that utilizes only critical machining parameters has a prediction accuracy of 96.35% while the one that utilizes only AE signal parameters has a prediction accuracy of 84.43%. It is observed that the order of parameter significance from the most to the least significant is as follows: feed rate, cutting speed, peak rate, AErms, depth of cut and prominent frequency.

INFLUENCE OF ACOUSTIC-ELECTRONIC CHANNEL ON ACOUSTIC EMISSION HIT PARAMETERS

2020 ◽  
pp. 14-22
Author(s):  
A. A. Sazonov ◽  
V. I. Shelobkov ◽  
V. I. Ivanov
Keyword(s):  
Acoustic Emission ◽  
Significant Influence ◽  
Signal Propagation ◽  
Propagation Channel ◽  
Object State ◽  
Signal Parameters ◽  
Ae Signal ◽  
Electronic Channel ◽  
Ae Signals ◽  
Electronic Channels

The paper deals with the influence of acoustic emission (AE) signal propagation channel on the parameters of these signals, which are used to judge the object state. It is shown that the acoustic channel, which includes the testing object and the acoustic emission transducer, has a significant influence on the parameters of AE signals. This must be taken into account, both in the interpretation of signals and in the calibration of acoustic emission transducers. Specific examples of degradation of AE signal parameters during the passage of acoustic-electronic channels are shown.

scholarly journals Influence of Frequency and Distance on Acoustic Emission Velocity Propagating in Various Dielectrics

2020 ◽  
Vol 10 (9) ◽  
pp. 3305
Author(s):  
Daria Wotzka
Keyword(s):  
Acoustic Emission ◽  
Nonlinear Regression ◽  
Modulation Frequency ◽  
Partial Discharges ◽  
Time Of Arrival ◽  
Dielectric Liquids ◽  
Signal Parameters ◽  
Two Factors ◽  
Ae Signal

The results obtained for the measurements of acoustic emission (AE) signal parameters emitted in three dielectric liquids are presented in this paper. In particular, the velocity of AE wave was calculated based on the time of arrival of the AE signal. A frequency modulated signal was generated by a piezoelectric transducer and measured at various distances by a hydrophone. The changes in velocity values at particular distances and for different frequencies were investigated. The analyses include the determination of the dependency of the velocity values from the distance between the communicating devices. A nonlinear regression model was calculated, and the differences between AE velocities propagating in the considered dielectrics were determined. Similarly, the influence of modulation frequency on the AE velocity was determined using nonlinear regression. Based on the calculation data, it can clearly be stated that the velocity of AE wave depends significantly on the frequency and distance at which it is registered. These two factors may have an important influence on the localization of partial discharges (PD) occurring in these types of dielectric liquids.

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 The duration-energy-size enigma for acoustic emission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Blai Casals ◽  
Karin A. Dahmen ◽  
Boyuan Gou ◽  
Spencer Rooke ◽  
Ekhard K. H. Salje
Keyword(s):  
Acoustic Emission ◽  
Mean Field Theory ◽  
Mean Field ◽  
Maximum Amplitude ◽  
Time Profile ◽  
Power Law Distribution ◽  
Model Predictions ◽  
Ae Signal ◽  
Strain Signal ◽  
Better Than

AbstractAcoustic emission (AE) measurements of avalanches in different systems, such as domain movements in ferroics or the collapse of voids in porous materials, cannot be compared with model predictions without a detailed analysis of the AE process. In particular, most AE experiments scale the avalanche energy E, maximum amplitude Amax and duration D as E ~ Amaxx and Amax ~ Dχ with x = 2 and a poorly defined power law distribution for the duration. In contrast, simple mean field theory (MFT) predicts that x = 3 and χ = 2. The disagreement is due to details of the AE measurements: the initial acoustic strain signal of an avalanche is modified by the propagation of the acoustic wave, which is then measured by the detector. We demonstrate, by simple model simulations, that typical avalanches follow the observed AE results with x = 2 and ‘half-moon’ shapes for the cross-correlation. Furthermore, the size S of an avalanche does not always scale as the square of the maximum AE avalanche amplitude Amax as predicted by MFT but scales linearly S ~ Amax. We propose that the AE rise time reflects the atomistic avalanche time profile better than the duration of the AE signal.

Superplastic Behavior in Ultrafine-Grained Materials Produced by Equal-Channel Angular Pressing

2008 ◽  
Vol 579 ◽  
pp. 29-40 ◽  
Author(s):  
Cheng Xu ◽  
Megumi Kawasaki ◽  
Roberto B. Figueiredo ◽  
Zhi Chao Duan ◽  
Terence G. Langdon
Keyword(s):  
Equal Channel Angular Pressing ◽  
High Strain ◽  
Processing Method ◽  
Strain Rates ◽  
Bulk Materials ◽  
Superplastic Behavior ◽  
Angular Pressing ◽  
Ultrafine Grained ◽  
Ultrafine Grained Materials ◽  
Aluminum And Magnesium Alloys

Equal-channel angular pressing (ECAP) is a convenient processing method for refining the grain size of bulk materials to the submicrometer level. Metallic alloys processed by ECAP often exhibit excellent superplastic characteristics including superplasticity at high strain rates. This paper summarizes recent experiments designed to evaluate the occurrence of superplasticity in representative aluminum and magnesium alloys and in the Zn-22% Al eutectoid alloy.

Acoustic Emission Analysis of Nanoindentation-Induced Fracture Events

2004 ◽  
Vol 841 ◽  
Author(s):  
Pawel Dyjak ◽  
Raman P. Singh
Keyword(s):  
Acoustic Emission ◽  
Brittle Materials ◽  
Signal Amplitude ◽  
Crack Arrest ◽  
Quantitative Information ◽  
Local Failure ◽  
Emission Analysis ◽  
Specimen Holder ◽  
Cube Corner ◽  
Ae Signal

ABSTRACTMonitoring of acoustic emission (AE) activity was employed to characterize the initiation and progression of local failure processes during nanoindentation-induced fracture. Specimens of various brittle materials were loaded with a cube-corner indenter and AE activity was monitored during the entire loading and unloading event using an AE transducer mounted inside the specimen holder. As observed from the nanoindentation and AE response, there were fundamental differences in the fracture behavior of the various materials. Post-failure observations were used to identify particular features in the AE signal (amplitude, frequency, rise-time) that correspond to specific types of fracture events. Furthermore, analysis of the parametric and transient AE data was used to establish the crack-initiation threshold, crack-arrest threshold, and energy dissipation during failure. It was demonstrated that the monitoring of AE signals yields both qualitative and quantitative information regarding highly local failure events in brittle materials.

The New SPD Processing Trends to Fabricate Bulk Nanostructured Materials

2006 ◽  
Vol 114 ◽  
pp. 7-18 ◽  
Author(s):  
Ruslan Valiev
Keyword(s):  
Plastic Deformation ◽  
Severe Plastic Deformation ◽  
Grain Refinement ◽  
Nanostructured Materials ◽  
Nanocrystalline Materials ◽  
Commercial Production ◽  
Materials Processing ◽  
Ultrafine Grained ◽  
Ultrafine Grained Materials ◽  
Bulk Nanocrystalline

During the last decade severe plastic deformation (SPD) has become a widely known method of materials processing used for fabrication of ultrafine-grained materials with attractive properties. Nowadays SPD processing is rapidly developing and is on the verge of a transition from lab-scale research to commercial production. This paper focuses on several new trends in the development of SPD techniques for effective grain refinement, including those for commercial alloys and presents new SPD processing routes to produce bulk nanocrystalline materials.

Study on Acoustic Emission of 1-3 Orthotropic Cement Based Piezoelectric Sensors

2012 ◽  
Vol 487 ◽  
pp. 471-475 ◽  
Author(s):  
Shi Hui Xie ◽  
Mi Mi Li ◽  
Mei Juan Zhou ◽  
Min Sun ◽  
Shi Feng Huang
Keyword(s):  
Acoustic Emission ◽  
Volume Fraction ◽  
Signal Strength ◽  
Signal Wave ◽  
Functional Material ◽  
Wave Shape ◽  
Sensing Element ◽  
Piezoelectric Composites ◽  
Ae Signal ◽  
Amplitude Decrease

1-3 orthotropic cement based piezoelectric composites were fabricated by cut-filling and arrange-filling technique, using PZT-51 ceramic as functional material and cement as passive matrix. 1-3 orthotropic cement based piezoelectric composites were prepared into Acoustic Emission (AE) sensors, the attenuation of AE signal on the concrete and the response of different sensors on the concrete with increasing distance were researched. The results showed that the signal strength received by sensing element increases with the increasing PZT volume fraction; signal peaks and amplitude decrease gradually when the testing distance increases; signal strength received on the ceramic title is stronger than on the concrete; the attenuation of signal wave shape received on the concrete is much slower when compared with ceramic title.

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