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.

Fabrication of Acoustic Emission Sensor Integrated with Cantilever Array for Detection of Signals Divided into Frequency Domain

2012 ◽  
Vol 523-524 ◽  
pp. 575-580 ◽  
Author(s):  
Takahiro Kawashima ◽  
Atsushi Matsui ◽  
Kazuo Muto ◽  
Moeto Nagai ◽  
Takayuki Shibata
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Frequency Domain Analysis ◽  
Acoustic Emission Sensor ◽  
Batch Fabrication ◽  
Ae Signal ◽  
Cantilever Array ◽  
Detection Of Signals ◽  
Ae Signals

In order to detect acoustic emission (AE) signals which are transient elastic waves generated by rapid release of strain energy derived from deformation in materials etc., general AE sensors were fabricated by using a piezoelectric film for detection of AE signals. However, these sensors required frequency domain analysis after recording AE signal. Therefore, this research has been developing an AE sensor integrated with cantilever array with different resonant frequencies for detection of AE signals divided into frequency domain by using MEMS techniques. In this paper, a design of cantilever structures was executed. Theoretical analysis and simulation using ANSYS software revealed that a resonant frequency of a cantilever was increased with decrease of its length in the range from 100 k to 1 MHz. Therefore, fabrication and frequency characterization of a cantilever array fabricated in our batch fabrication process were executed.

Detection of corrosion on 304 stainless steel by acoustic emission measurement

2010 ◽  
Vol 57 (3) ◽  
pp. 126-132 ◽  
Author(s):  
Gang Du ◽  
Weikui Wang ◽  
Shizhe Song ◽  
Shijiu Jin
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Constant Load ◽  
Corrosion Process ◽  
304 Stainless Steel ◽  
Emission Measurement ◽  
Nacl Solution ◽  
Content Type ◽  
Ae Signal ◽  
Ae Signals

PurposeThe purpose of this paper is to report an investigation of the acoustic emission (AE) characteristics of the corrosion process of 304 stainless steel in acidic NaCl solution.Design/methodology/approachThe corrosion behavior of a specimen with constant load in acidic NaCl solution was studied, and the AE signal characteristics of the corrosion process were analyzed. Stress corrosion cracking of the specimen was detected using the AE and electrochemical noise (EN) techniques, and the acquired data were compared.FindingsThe results indicated that AE technology is very sensitive to the AE signals generated by 304 nitrogen controlled stainless steel in acidic NaCl solution. The characteristics of AE signals at different stages of the corrosion process are significantly different. Additionally, the AE test result is confirmed by the EN test results.Originality/valueThe characteristics of AE signals at different stages of the corrosion process are gained for the first time, which is an important guide by which to distinguishing different stages of corrosion.

scholarly journals Analysis of a Sound Signal for Quality Monitoring in Laser Microlap Welding

2020 ◽  
Vol 10 (6) ◽  
pp. 1934
Author(s):  
Bo-Si Kuo ◽  
Ming-Chyuan Lu
Keyword(s):  
Standard Deviation ◽  
Monitoring System ◽  
Signal Analysis ◽  
Joint Strength ◽  
Metal Sheet ◽  
Sound Signal ◽  
Welding Quality ◽  
Signal Features ◽  
Metal Sheets ◽  
Laser Microwelding

This study focused on correlation analysis between welding quality and sound-signal features collected during microlaser welding. The study provides promising features for developing a monitoring system that detects low joint strength caused by a gap between metal sheets after welding. To obtain sound signals for signal analysis and develop the monitoring system, experiments for laser microlap welding were conducted on a laser microwelding platform by installing a microelectromechanical system (MEMS) microphone away from the welding point, and an acoustic emission (AE) sensor on the fixture. The gap between two metal sheet layers was controlled using clamp force, a pressing bar, and the appropriate installation of a thin piece of paper between the metal sheets. After sound signals from the microphone were collected, the correlation between features of time-domain sound signals and of welding quality was analyzed by categorizing the referred signals into eight sections during welding. After appropriately generating the features after signal analysis and selecting the most promising features for low-joint-strength monitoring on the basis of scatter index J, a hidden Markov model (HMM)-based classifier was applied to evaluate the performance of the selected sound-signal features. Results revealed that three sound-signal features were closely related to joint-strength variation caused by the gap between two metal-sheet layers: (1) the root-mean-square (RMS) value of the first section of sound signals, (2) the standard deviation of the first section of sound signals, and (3) the standard deviation to the RMS ratio of the second section of sound signals. In system evaluation, a 100% classification rate was obtained for normal and low-bonding-strength monitoring when the HMM-based classifier was developed on the basis of the three selected features.

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.

Research on Feature Extraction Method Based on Rub-Impact Acoustic Emission Signals of Mechanical Seal End Faces

2010 ◽  
Vol 36 ◽  
pp. 68-74
Author(s):  
Chuan Jun Liao ◽  
Shuang Fu Suo ◽  
Wei Feng Huang
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Mechanical Seal ◽  
Mechanical Seals ◽  
Feature Extraction Method ◽  
Different Types ◽  
Ae Signal ◽  
Feature Information ◽  
Ae Signals

Acoustic emission (AE) techniques are put forward to monitor rub-impacts between rotating rings and stationary rings of mechanical seals by this paper. By analyzing feature extraction methods of the typical rub-impact AE signal, the method combining of wavelet scalogram and power spectrum is found useful, and can used to attribute the feature information implicated in rub-impact AE signals of mechanical seal end faces. Both simulations and experimental research prove that the method is effective, and are used successfully to identify the typical features of different types of rub-impacts of mechanical seal end faces.

scholarly journals Monitoring of Changes Signal Acoustic Emission Signals Using Waveguides

2017 ◽  
Vol 65 (4) ◽  
pp. 1317-1322 ◽  
Author(s):  
Jaroslav Začal ◽  
Petr Dostál ◽  
Michal Šustr ◽  
David Dobrocký
Keyword(s):  
Experimental Study ◽  
Stainless Steel ◽  
Acoustic Emission ◽  
Signal Detection ◽  
Piezoelectric Sensors ◽  
Material Surface ◽  
Medium Density ◽  
Medium Density Fibreboard ◽  
Ae Signal ◽  
Signal Dampening

This paper is focused on possibilities of acoustic emission (AE) signal detection from material surface through waveguide for commonly used piezoelectric sensors. It also considers the experimental study of enhanced detection of occurrence of signal guided through waveguide corpus, its changes and deformities. Aim of this work is verification of several waveguide setup possibilities for maximization of AE signal detection in practice. For this purpose, multiple waveguide setups were manufactured from stainless steel and aluminium alloy. Hsu‑Nielson pen test was utilized for signal actuation. Results demonstrate the differences between measured AE signal with and without employment of waveguide (changes in signal course through different materials and shapes), as well as magnitude of signal dampening and amplification necessary for veritable signal interpretation. Measurements were conducted on agglomerated composite of medium density fibreboard (MDF).

Reciprocating compressor valve damage estimation under varying speeds through the acoustic emission technique

2019 ◽  
Vol 10 (5) ◽  
pp. 621-633
Author(s):  
Hoi-Yin Sim ◽  
Rahizar Ramli ◽  
Ahmad Saifizul
Keyword(s):  
Acoustic Emission ◽  
Regression Analysis ◽  
Flow Rate ◽  
Reciprocating Compressor ◽  
Damage Estimation ◽  
Content Type ◽  
Air Compressor ◽  
Ae Signal ◽  
Valve Opening ◽  
Ae Signals

Purpose The purpose of this paper is to examine the effect of reciprocating compressor speeds and valve conditions on the roor-mean-square (RMS) value of burst acoustic emission (AE) signals associated with the physical motion of valves. The study attempts to explore the potential of AE signal in the estimation of valve damage under varying compressor speeds. Design/methodology/approach This study involves the acquisition of AE signal, valve flow rate, pressure and temperature at the suction valve of an air compressor with speed varrying from 450 to 800 rpm. The AE signals correspond to one compressor cycle obtained from two simulated valve damage conditions, namely, the single leak and double leak conditions are compared to those of the normal valve plate. To examine the effects of valve conditions and speeds on AE RMS values, two-way analysis of variance (ANOVA) is conducted. Finally, regression analysis is performed to investigate the relationship of AE RMS with the speed and valve flow rate for different valve conditions. Findings The results showed that AE RMS values computed from suction valve opening (SVO), suction valve closing (SVC) and discharge valve opening (DVO) events are significantly affected by both valve conditions and speeds. The AE RMS value computed from SVO event showed high linear correlation with speed compared to SVC and DVO events for all valve damage conditions. As this study is conducted at a compressor running at freeload, increasing speed of compressor also results in the increment of flow rate. Thus, the valve flow rate can also be empirically derived from the AE RMS value through the regression method, enabling a better estimation of valve damages. Research limitations/implications The experimental test rig of this study is confined to a small pressure ratio range of 1.38–2.03 (free-loading condition). Besides, the air compressor is assumed to be operated at a constant speed. Originality/value This study employed the statistical methods namely the ANOVA and regression analysis for valve damage estimation at varying compressor speeds. It can enable a plant personnel to make a better prediction on the loss of compressor efficiency and help them to justify the time for valve replacement in future.

scholarly journals Friction and Wear Monitoring Methods for Journal Bearings of Geared Turbofans Based on Acoustic Emission Signals and Machine Learning

Lubricants ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 29 ◽  
Author(s):  
Noushin Mokhtari ◽  
Jonathan Gerald Pelham ◽  
Sebastian Nowoisky ◽  
José-Luis Bote-Garcia ◽  
Clemens Gühmann
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Friction And Wear ◽  
Journal Bearing ◽  
Journal Bearings ◽  
Wear Volume ◽  
Time Frequency ◽  
Ae Signal ◽  
Ae Signals

In this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (AE) signals. The three friction states: dry (boundary), mixed, and fluid friction of journal bearings are classified by pre-processing the AE signals with windowing and high-pass filtering, extracting separation effective features from time, frequency, and time-frequency domain using continuous wavelet transform (CWT) and a Support Vector Machine (SVM) as the classifier. Furthermore, it is shown that journal bearing friction classification is not only possible under variable rotational speed and load, but also under different oil viscosities generated by varying oil inlet temperatures. A method used to identify the location of occurring mixed friction events over the journal bearing circumference is shown in this paper. The time-based AE signal is fused with the phase shift information of an incremental encoder to achieve an AE signal based on the angle domain. The possibility of monitoring the run-in wear of journal bearings is investigated by using the extracted separation effective AE features. Validation was done by tactile roughness measurements of the surface. There is an obvious AE feature change visible with increasing run-in wear. Furthermore, these investigations show also the opportunity to determine the friction intensity. Long-term wear investigations were done by carrying out long-term wear tests under constant rotational speeds, loads, and oil inlet temperatures. Roughness and roundness measurements were done in order to calculate the wear volume for validation. The integrated AE Root Mean Square (RMS) shows a good correlation with the journal bearing wear volume.

Acoustic Emission During Athermal Martensitic Transformation in Steels

1990 ◽  
Vol 112 (1) ◽  
pp. 84-91 ◽  
Author(s):  
Xiangying Liu ◽  
Elijah Kannatey-Asibu
Keyword(s):  
Free Energy ◽  
Acoustic Emission ◽  
Martensitic Transformation ◽  
Signal Intensity ◽  
Temperature Derivative ◽  
Process Characteristics ◽  
Ae Signal ◽  
The Relationship ◽  
Ae Signals ◽  
Start Temperature

A relationship developed earlier between acoustic emission signals and the process of athermal martensitic transformation based on the free energy associated with the process is extended and verified experimentally. The relationship is found to model the process characteristics very well. The intensity of AE signal generated during transformation was found to be proportional to the temperature derivative of the fraction of martensite, the cooling rate, and volume of specimen. The AE signal was also found to be related to the carbon content of the steel. During transformation, the signal intensity was found to increase to a peak, and then tail off near the end of the transformation. Values of the martensite start temperature obtained from plots of the total RMS squared AE signals were also found to correlate well with values from the literature.

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