Extracting Acoustic Emission Signal Feature of Grinding Processing

2014 ◽  
Vol 887-888 ◽  
pp. 1175-1178
Author(s):  
Xiao Jing Huo ◽  
Jia Xu Teng ◽  
Wen Di Wang ◽  
Ai Min Shen ◽  
Jun Wei Yang
Keyword(s):  
Acoustic Emission ◽  
High Hardness ◽  
High Sensitivity ◽  
Local Area ◽  
Grinding Process ◽  
Emission Signal ◽  
Ae Signal ◽  
Power Spectrum Method ◽  
Low Amplitude ◽  
Elastic Stress Wave

Acoustic Emission (AE) may be defined as a transient elastic stress wave generated by the rapid release of strain energy in local area of material. To overcome the limitation of some traditional techniques, the AE technique, which provides high sensitivity and responding speed, were developed in the present paper. AE signature is usually difficult to be extracted and characterized in grinding process of 1Cr18Ni9Ti coatings due to their high hardness, great ductility, inhomogeneous structure and irregular surface with lots of hard points and pores. In this paper, AE signal of stationary grinding status before wheel-workpiece contact was characterized first, then AE signal of the grinding process was analyzed using root-mean-square (RMS) and power spectrum method. Results showed that before the contact occurred, the grinding signal is stable, with low amplitude and frequency ranging all frequency channels and no peak signal. However, when contact occurred, the RMS and spectrum of AE signal increased obviously and the bandwidth varied exquisitely between 100 KHz and 300 KHz. The real contact time between wheel and workpiece was about 0.5 to 1 ms.

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.

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.

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.

Study on Acoustic Emission in the Grinding Process Automation

1999 ◽  
Author(s):  
Ming Chen ◽  
Bing-Yuan Xue
Keyword(s):  
Acoustic Emission ◽  
High Efficiency ◽  
Grinding Wheel ◽  
Grinding Process ◽  
Process Automation ◽  
Grinding Burn ◽  
Sensing System ◽  
Monitoring Technique ◽  
Ae Signal ◽  
Process Prediction

Abstract Comprehensive experiments have been conducted to investigate the monitoring technique for grinding process automation with acoustic emission (AE) signal. The AE signal generated during the grinding process is analyzed to determine its sensitivity to process. The detection of contact between the grinding wheel and workpiece and in-process prediction of grinding burn have been discussed in sequence. The results have been obtained as follows: (1) AE contact detector can save the non-machine time remarkably, thus high efficiency is available. (2) An effective intelligent sensing system has been developed and grinding burn can predicted. As mentioned above, AE technique has found wide applications in the grinding process automation.

Spectrum Analysis of Acoustic Emission Signal Obtained during Breakaway Oxidation of 2.25Cr-1Mo Steel

2009 ◽  
Vol 293 ◽  
pp. 27-39 ◽  
Author(s):  
B.B. Jha ◽  
Barada Kanta Mishra ◽  
S.N. Ojha
Keyword(s):  
Acoustic Emission ◽  
Spectrum Analysis ◽  
Oxide Scales ◽  
Frequency Pattern ◽  
Frequency Spectra ◽  
Breakaway Oxidation ◽  
Emission Signal ◽  
Ae Signal ◽  
Characteristic Features ◽  
Physical Phenomena

Frequency spectrum analysis of acoustic emission (AE) signal has been carried out during breakaway oxidation and internal cracking of oxide scales formed on 2.25Cr-1Mo steel. Three regions viz pre-breakaway, post-breakaway and internal cracking of scales have been distinguished based upon thermogravimetric analysis and SEM/EPMA observations. The frequency pattern of the AE signal obtained in three different regions shows three different characteristic features. Frequency spectra based upon the predominant frequencies have been correlated with the physical phenomena occurring during the course of oxidation.

Study on Porcelain Acoustic Emission Signal Based on All Phase Spectrum Analysis

2011 ◽  
Vol 391-392 ◽  
pp. 569-574
Author(s):  
Ding Ye ◽  
Wei Jin ◽  
Chen Xi Liu
Keyword(s):  
Acoustic Emission ◽  
Spectrum Analysis ◽  
Weighted Average ◽  
Phase Spectrum ◽  
Truncation Method ◽  
Emission Signal ◽  
Energy Leakage ◽  
Phase Signal ◽  
Ae Signal

In order to differentiate the porcelain quality, the paper introduces the All Phase spectrum analysis technology and studies on analyzing porcelain acoustic emission (AE) signal. As for the energy leakage by traditional signal truncation method in processing the signal, the all phase truncation method somewhat reduce the leakage which affects the follow-up porcelain quality discrimination. All instances consisting sample point are considered and weighted average technology is introduced to make amplitude-frequency clearer. According to the simulation, the energy leakage based on all phase signal processing is weakened and the spectrum is able to be accurate. It is more beneficial to the follow-up porcelain quality discrimination.

scholarly journals Type-2 Fuzzy Modeling for Acoustic Emission Signal in Precision Manufacturing

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Qun Ren ◽  
Luc Baron ◽  
Marek Balazinski
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Fuzzy Modeling ◽  
Machining Process ◽  
Precision Machining ◽  
Precision Manufacturing ◽  
Emission Signal ◽  
Tool Condition ◽  
Ae Signal

This paper presents an application of type-2 fuzzy logic on acoustic emission (AE) signal modeling in precision manufacturing. Type-2 fuzzy modeling is used to identify the AE signal in precision machining. It provides a simple way to arrive at a definite conclusion without understanding the exact physics of the machining process. Moreover, the interval set of the output from the type-2 fuzzy approach assesses the information about the uncertainty in the AE signal, which can be of great value for investigation of tool wear conditions. Experiments show that the development of the AE signal uncertainty trend corresponds to that of the tool wear. Information from the AE uncertainty scheme can be used to make decisions or investigate the tool condition so as to enhance the reliability of tool wear.

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