scholarly journals Failure mechanism and acoustic emission signal characteristics of coatings under the condition of impact indentation

2019 ◽  
Vol 38 (2019) ◽  
pp. 601-611
Author(s):  
Dong Tian-Shun ◽  
Wang Ran ◽  
Li Guo-Lu ◽  
Liu Ming
Keyword(s):  
Acoustic Emission ◽  
Surface Morphology ◽  
Failure Mechanism ◽  
Bonding Strength ◽  
Cross Sectional ◽  
Emission Signal ◽  
Signal Characteristics ◽  
Interface Cracking ◽  
Ae Signal ◽  
The Impact

AbstractIn this work, the substrate, NiCr coating, Al2O3 coating with NiCr undercoating and Al2O3 coating were tested by an impact indentation device equipped with an acoustic emission (AE) detection equipment. The surface morphology, dimension, cross-sectional image, 3D topography of indention and bonding strength of coatings were analyzed. The failure mechanism and AE signal characteristics of the coatings under impact were studied. The results demonstrate that the failure mode of NiCr coating was dominated by interface cracking, and that of Al2O3 coating is fracture and accompanied by a small amount of interface cracking, while Al2O3 coating with NiCr undercoating possesses common characteristics of the first two. The energy counting and wave voltage of AE signal were more sensitive to the bonding strength of coating in the impact process, which can be used to characterize the bonding strength of coating.

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.

Acoustic Emission Signal Classification Using Feature Analysis and Deep Learning Neural Network

2020 ◽  
pp. 2150030
Author(s):  
Jian-Da Wu ◽  
Yu-Han Wong ◽  
Wen-Jun Luo ◽  
Kai-Chao Yao
Keyword(s):  
Acoustic Emission ◽  
Deep Learning ◽  
Frequency Domain ◽  
Acoustic Emission Signal ◽  
Signal Classification ◽  
Learning Method ◽  
Identification Rate ◽  
Emission Signal ◽  
Identification Time ◽  
The Impact

With the development of artificial intelligence in recent years, deep learning has been widely used in mechanical system signal classification but the impact of different feature extractions on the efficiency and effectiveness of deep learning neural networks is more important. In this study, a vehicle classification based on engine acoustic emission signal in the time domain, the frequency domain and the wavelet transform domain for deep learning network techniques is presented and compared. In signal classification, different feature extractions will show in different decomposition levels and can be used to recognize the various acoustic conditions. In the experimental work, as engines from 10 different ground vehicles operate, the measured sound signal is converted into a digital signal, and the established data set is classified and identified by the deep learning method. The number of samples, identification rate and identification time in the various signal domains are compared and discussed in this study. Finally, the experimental results and data analysis show that by using the wavelet signal and the deep learning method, excellent identification time and identification rate can be achieved, compared with traditional time and frequency domain signals.

On-Line Monitoring the Hard Turning Using Distribution Parameters of Acoustic Emission Signal

2015 ◽  
Vol 787 ◽  
pp. 907-911
Author(s):  
J. Bhaskaran
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Hard Turning ◽  
Metal Cutting ◽  
Cubic Boron Nitride ◽  
Emission Signal ◽  
Distribution Parameters ◽  
On Line ◽  
Ae Signal ◽  
Wear Monitoring

In hard turning, tool wear of cutting tool crossing the limit is highly undesirable because it adversely affects the surface finish. Hence continuous, online tool wear monitoring during the process is essential. The analysis of Acoustic Emission (AE) signal generated during conventional machining has been studied by many investigators for understanding the process of metal cutting and tool wear phenomena. In this experimental study on hard turning, the skew and kurtosis parameters of root mean square values of AE signal (AERMS) have been used for online monitoring of a Cubic Boron Nitride (CBN) tool wear.

Acoustic Emission (AE) Propagation Attenuation Theory and Rule in Non-Perfect Elastic Coal and Rock

2013 ◽  
Vol 477-478 ◽  
pp. 620-623
Author(s):  
Guo Wei Dong
Keyword(s):  
Numerical Simulation ◽  
Acoustic Emission ◽  
Quality Factor ◽  
Field Test ◽  
Propagation Distance ◽  
Signal Propagation ◽  
Emission Signal ◽  
Ae Signal ◽  
Propagation Rule ◽  
Quality Factor Q

Propagation rule of acoustic emission (AE) signal in coal and rock is an important basis when AE technique forecasts coal and rock dynamical disasters. Based on correlative theory of quality factor Q, Acoustic emission signal propagation attenuation formula in non-perfect elastic coal and rock are analyzed, Based on the theoretic formula, Effects of different quality factor and propagation distance on AE propagation attenuation are theoretically analyzed ;Based on theoretic analysis results, AE signal propagation numerical simulation and field test programs are designed, AE signal propagation rules in elastoplastic coal and rock are obtained. Field test and numerical simulation experimentation results validate rationality of theoretic forumla. Study production can guide AE technique that forecasts mine and rock dynamical disasters.

scholarly journals Investigation on recognition method of acoustic emission signal of the compressor valve based on CNN and LSTM network

2021 ◽  
Vol 252 ◽  
pp. 02023
Author(s):  
Yanfeng Wang ◽  
Jin Wang ◽  
Junwei Sun ◽  
Enhao Liang ◽  
Tao Wang
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Dynamic Characteristics ◽  
Short Term Memory ◽  
Reciprocating Compressor ◽  
Short Term ◽  
Emission Signal ◽  
Ae Signal ◽  
Long Short Term Memory ◽  
Lstm Network

The valve is one of the important parts of the reciprocating compressor, which directly affects the thermodynamic process and reliability of the compressor. In this paper, acoustic emission (AE) technology is used to predict the dynamic characteristics of valves. The AE signal of the compressor valve is analyzed based on the deep learning method, and the mapping relation between the AE signal and the dynamic characteristics of the valve is obtained. The results show that the prediction accuracy of the models trained by Long Short-Term Memory (LSTM) artificial neural network and Convolutional Neural Network (CNN) is 97% and 95%, respectively, which can accurately predict the dynamic characteristics of the valve. Although the prediction results of CNN are slightly lower than that of LSTM network, the calculation speed of CNN is relatively faster.

Monitoring of the High-Technology Nailing of CFRTP Material under Ultrasonic Vibration by Acoustic Emission Method

2020 ◽  
Vol 1009 ◽  
pp. 25-30
Author(s):  
Yoshiaki Akematsu ◽  
Hiromitsu Gotho ◽  
Takayuki Tani ◽  
Hideaki Murayama ◽  
Tsuyoshi Matsuo ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Ultrasonic Vibration ◽  
Energy Parameter ◽  
High Technology ◽  
Acoustic Emission Method ◽  
Emission Method ◽  
Thermoplastic Material ◽  
Signal Characteristics ◽  
Ae Signal ◽  
Ae Signals

In this study, the potential to monitor the high-technology nailing of carbon fiber reinforced thermoplastic material (CFRTP) under ultrasonic vibration was investigated by acoustic emission (AE) method. AE signals were detected by a piezoelectric AE sensor during high-technology nailing under ultrasonic vibration. This paper describes some experimental results on AE signal characteristics and observation of the high-technology nailing. In order to investigate the effects of machining condition, we focused on RMS voltage, which is dependent on the energy parameter of the AE signal. It was found that the AE method is a useful method of monitoring high-technology nailing.

scholarly journals Electrical discharge phenomenon of electrode gaps in oil insulation

2018 ◽  
Vol 197 ◽  
pp. 11005
Author(s):  
Jannus Maurits Nainggolan ◽  
MK Iwa Ganiwa ◽  
Chairul Hudaya ◽  
Amien Rahardjo
Keyword(s):  
Acoustic Emission ◽  
Signal Analysis ◽  
Electrical Discharge ◽  
Frequency Component ◽  
Solid Layer ◽  
Insulating Material ◽  
Emission Signal ◽  
Plane Electrode ◽  
Maximum Value ◽  
Ae Signal

An electrical discharge is a phenomenon of ionization of an insulating material. Ionization can occur when the stress applied to the insulating material begins to close to the maximum value of stress can be restrained. In this study, a high voltage was given on a point-plane electrode that would produce ionization (discharge) on the gap of the electrode. The point-plane electrode was placed in an iron tank containing oil insulation. The distance of a gap between the electrodes varies from 2 mm to 4 mm. Then, the signal from the occurrence of electrical discharge was capture using an acoustic emission (AE) sensor placed on the outside of the tank wall. The detected acoustic emission signal was amplified with a 40 dB amplifier, so the signal would be easier to analyze. At the other condition, a solid layer of insulation with a thickness of 4 mm would also be placed on the gap the electrode. The result of the signal analysis showed small differences in the intensity of the detected AE signal at all the distance of electrode gaps. The main frequency component of the detected AE signal at all electrode gaps was several hundred kilohertz.

Investigation of the Peculiarities of Deformation and Fracture of Metals Obtained on the Basis of 3D Printing with Registration of an Acoustic Emission Signal

2021 ◽  
Vol 1037 ◽  
pp. 71-76
Author(s):  
Maksim S. Anosov ◽  
Yury G. Kabaldin ◽  
Dmitrii A. Shatagin ◽  
Dmitry A. Ryabov ◽  
Pavel Kolchin
Keyword(s):  
Acoustic Emission ◽  
3D Printing ◽  
Test Temperature ◽  
Acoustic Emission Signal ◽  
Fracture Mechanisms ◽  
Characteristic Parameters ◽  
Diagnostic Features ◽  
Emission Signal ◽  
Deformation And Fracture ◽  
Ae Signal

The paper investigates the features of deformation and fracture of steels obtained using the technology of 3D printing by electric arc surfacing based on the registration of the acoustic emission signal. With a decrease in the test temperature of 07Cr25Ni13 steel, a decrease in the work expended in stretching the specimen is observed, both at the stage of elastic deformation and at the stage of strain hardening. It was found that the most informative characteristic parameters of the AE signal include: the pulse count rate N, the total count NΣ, and the AE signal entropy. With a decrease in the test temperature, there is a significant increase in the intensity of the AE signal, the total number of pulses at all stages of deformation and destruction of steel. The obtained regularities of changes in the characteristic parameters of the AE signal can be used as diagnostic features, both in assessing the stage of deformation and destruction of the material, and the structural state of the material. Fractographic studies have shown a significant decrease in the tough component of 08Mn2Si steel with a decrease in the test temperature. The fracture mechanisms of 07Cr25Ni13 steel change insignificantly with decreasing temperature, however, a significant decrease in the ductility of the metal is observed, as evidenced by a decrease in the size of ductile fracture cups.

The Impact of Surface Integrity by Hard Turning vs. Grinding on Rolling Contact Fatigue

2007 ◽  
Author(s):  
A. W. Warren ◽  
Y. B. Guo
Keyword(s):  
Acoustic Emission ◽  
Surface Integrity ◽  
Hard Turning ◽  
Rolling Contact Fatigue ◽  
Contact Fatigue ◽  
Fatigue Tests ◽  
Rolling Contact ◽  
Polished Surface ◽  
Emission Signal ◽  
The Impact

Hard turning and grinding are finishing processes for the manufacture of precision components such as bearings, gears, and cams. However, the effects of distinct surface integrity by hard turning vs. grinding on rolling contact life are poorly understood. Four representative surface types were prepared: as-turned, as-ground, turned and polished, and ground and polished. Surface integrity was characterized by surface topography, microstructure, and micro/nanohardness. Fatigue tests were performed with an acoustic emission sensor and the signal processing software. The amplitude of acoustic emission signal is the most stable and sensitive signal to fatigue failure. The turned surface may have a longer life (>84%) than the ground one with equivalent surface finish.

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