Analysis of Acoustic Emission Signal Generation During Martensitic Transformation

1986 ◽  
Vol 108 (4) ◽  
pp. 328-331 ◽  
Author(s):  
Elijah Kannatey-Asibu ◽  
Dong Pingsha
Keyword(s):  
Acoustic Emission ◽  
Energy Content ◽  
High Strength Steels ◽  
High Strength ◽  
Signal Generation ◽  
Martensite Formation ◽  
Emission Signal ◽  
Chemical Free Energy ◽  
Ae Signal ◽  
Work Done

The formation of cold cracks during welding of high strength steels is almost always preceded by martensite formation. Real time detection of both the martensite and cracks formed is a basic necessity of automated welding systems. Acoustic emission has been found to be highly suited for this purpose. Unfortunately, most of the work done to date on AE generation during martensitic phase transformation has been qualitative in nature. This paper presents a quantitative analysis of AE signal generation during martensite formation, using an energy method. This formulation relates the chemical free energy change which is the driving force for the transformation to the RMS value which is a measure of the energy content of AE signals. The analysis shows that the RMS signal is dependent on carbon concentration, volume transformed, cooling rate, and temperature. This is consistent with previous experimental work.

Quantitative Relationships for Acoustic Emission from Orthogonal Metal Cutting

1981 ◽  
Vol 103 (3) ◽  
pp. 330-340 ◽  
Author(s):  
Elijah Kannatey-Asibu ◽  
David A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Metal Cutting ◽  
Energy Content ◽  
Energy Levels ◽  
Cutting Parameters ◽  
Signal Energy ◽  
Emission Signal ◽  
Orthogonal Metal Cutting ◽  
Ae Signal ◽  
Work Of Deformation

Theoretical relationships have been drawn between acoustic emission (AE) and the metal cutting process parameters by relating the energy content of the AE signal to the plastic work of deformation which generates the emission signals. The RMS value of the emission signal is expressed in terms of the basic cutting parameters. Results are presented for 6061-T6 aluminum and SAE 1018 steel over the range of speeds 25.2 to 372 sfm (0.128 to 1.9 m/s) and rake angles 10 to 40 deg. Good correlation has been found between predicted and experimental signal energy levels. In addition, AE generation from chip contact along the tool face is studied and the AE energy level reflects the existence of chip sticking and sliding on the tool face, and indicates the feasibility of utilizing AE in tool wear sensing.

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.

Characterization of Sheet Metal Forming Using Acoustic Emission

1990 ◽  
Vol 112 (1) ◽  
pp. 44-51 ◽  
Author(s):  
S. Y. Liang ◽  
D. A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Acoustic Emission Signal ◽  
Metal Forming ◽  
Energy Content ◽  
Analytical Relationship ◽  
Workpiece Material ◽  
Emission Signal

The application of acoustic emission signal analysis for characterization of sheet metal forming operations is discussed in this paper. Two particular sheet metal forming operations are examined: punch stretching and deep drawing. The acoustic emission signal characteristics, including the energy content, the spectral properties, and time series behaviors, as functions of the process state, are experimentally studied. Using plastic work analysis, an analytical relationship between acoustic emission energy rate and punch stretching parameters (punch feed rate, workpiece thickness, punch size, holder diameter, amount of plastic deformation, and workpiece material properties) is developed and supporting experimental results presented. During the forming processes, acoustic emission signal features show strong correlations with punch/workpiece contact, yielding, deformation, flange wrinkling, necking and fracture. Therefore, acoustic emission can be effectively used for in-process monitoring of sheet metal forming operations.

Modeling and Analysis of Acoustic Emission in Diamond Turning

1996 ◽  
Vol 118 (2) ◽  
pp. 199-207 ◽  
Author(s):  
J. J. Liu ◽  
D. A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Energy Content ◽  
Rolling Process ◽  
Quantitative Model ◽  
Diamond Turning ◽  
Modeling And Analysis ◽  
Process Characterization ◽  
Theoretical Predictions ◽  
Frequency Components ◽  
Ae Signal

To estimate the acoustic emission (AE) energy released in diamond turning, a quantitative model, which contains the energy from primary, secondary, tertiary cutting zones and the rubbing zones, is proposed and compared with experimental data. The purpose of this model is to assist in process characterization and monitoring. As part of the model developed here the plowing energy, that is, the energy released in the tertiary zone, is approximated by the forming load in the rolling process where the roller is stationary. This load is theoretically calculated by the upper bound method and used in the estimation. A series of diamond turning tests were conducted to check the validity of the model. It was found that the energy content of the AE signal is close to the theoretical predictions. The spectral analysis of the AE signal in these tests is also carried out. It was noticed that when the diamond tool first touches the workpiece without producing any chip, more high frequency components were observed and this stage was recognized as the rubbing stage. The results further support the previous findings, that is, that abnormal rubbing always increases the mean frequency of the raw AE signal.

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