• Journal of the Korean Society of Industry Convergence
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• v.2 no.2
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• pp.147-153
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• 1999

An in-process extraction method of the ground surface roughness is a bottle-neck and essential field in conventional machining process. We define the D.A.R.F(Dimensionless Average Roughness Factor) that has a roughness characteristic of ground surface. D.A.R.F include the absolute average and the standard deviation values which are the analytic parameters of the AE(Acoustic Emission) signal generated during the grinding operation. The theoretical equation between the surface roughness and the D.A.R.F has been derived from the linear regressive analysis and verified its availability through the experimentation on the surface grinding machine.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.5 s.98
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• pp.186-193
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• 1999

Sensor feedback for process control is one of the essential elements is an automated deburring procedure. This paper presents the implementation of acoustic emission (AE), which has been developed as a feedback sensing technique for precision (mechanical) deburring, in a precision laser deburring process. AE signals were sampled for laser machining/deburring under various experimental conditions and analyzed using several signal-processing methods including AErms and spectral analysis. The results, such as the sensitivity of AE signals for different laser cutting depths, edge detection capability and the frequency analysis show a clear correlation between physical process parameters and the AE signals. A subsequent control strategy for deburring automation is also briefly discussed.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.6
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• pp.509-519
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• 2007

This paper presents new techniques under development for monitoring the health and vibration of the active components in nuclear power plants, The purpose of this study is to develop an automated system for condition classification of a check valve one of the components being used extensively in a safety system of a nuclear power plant. Acoustic emission testing for a check valve under controlled flow loop conditions was performed to detect and evaluate disc movement for valve failure such as wear and leakage due to foreign object interference in a check valve, It is clearly demonstrated that the evaluation of different types of failure types such as disc wear and check valve leakage were successful by systematically analyzing the characteristics of various AE parameters, It is also shown that the leak size can be determined with an artificial neural network.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.2
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• pp.191-199
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• 2019

In this paper, dissimilar friction welding were produced using 15 mm diameter solid bar in chrome molybedenum steel(SCM440) to stainless steel(STS316L) to investigate their mechanical properties. Consequently, optimal welding conditions were n=2000 rpm, HP=70 MPa, UP=140 MPa, HT=10 sec and UT=10 sec when the metal loss(Mo) is 8.6 mm. In addition, an acoustic emission technique was applied to evaluate the optimal friction welding condition. AE parameters including the cumulative count, amplitude and energy showed a various changes according to the friction condition. A continuous type waveforms and low frequency spectrum was presented in friction time. On the other hand, a burst type waveform and high frequency spectrum was exhibited in pressing time.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.4
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• pp.69-76
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• 1994

AE(Acoustic Emission) signal is correlated to workpiece material, cutting conditions and tool geometry during metal cutting. The relationship between AE signal and cutting parameters can be obtained by theoretical model and experiments. The value of CR(Count Rate) is nearly constant in stable cutting, but when the chatter vibration occours, the value of CR is rapidly increased due to the vibration deformation zone. By experimental signal processing of AE, it is more effective than by RMS(Root Mean Square) measurement to detect the threshold of chatter vibration by CR measurement.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.12
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• pp.39-47
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• 2005

An acoustic emission technique is applied to the tensile tests of the plastic specimen under the different test speeds and the structural integrity evaluation of the plastic shell structure. Several AE characteristics are acquired from the tensile tests and they are proven to be useful parameters in evaluating its structural integrity. The results shows that tensile strength has almost constant value over some higher speed region while revealing some increasing tendency in strength as the test speeds up in lower speed region. The crack initiation loads and locations are accurately evaluated during the static compression testing of the plastic shell structures by using acoustic emission technique.

• Geomechanics and Engineering
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• v.25 no.2
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• pp.135-142
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• 2021

This study provided a new research perspective for processing and analyzing AE data to evaluate rock failure. Cluster method and information entropy theory were introduced to investigate temporal and spatial correlation of acoustic emission (AE) events during the rock failure process. Laboratory experiments of granite subjected to compression were carried out, accompanied by real-time acoustic emission monitoring. The cumulative length and dip angle curves of single links were fitted by different distribution models and distribution functions of link length and directionality were determined. Spatial scale and directionality of AE event distribution, which are characterized by two parameters, i.e., spatial correlation length and spatial correlation directionality, were studied with the normalized applied stress. The entropies of link length and link directionality were also discussed. The results show that the distribution of accumulative link length and directionality obeys Weibull distribution. Spatial correlation length shows an upward trend preceding rock failure, while there are no remarkable upward or downward trends in spatial correlation directionality. There are obvious downward trends in entropies of link length and directionality. This research could enrich mathematical methods for processing AE data and facilitate the early-warning of rock failure-related geological disasters.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.254-262
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• 2003

In this study, acoustic emission (AE) signals due to surface cracking and moisture movement in the flat-sawn boards of oak (Quercus Variablilis) during drying under the ambient conditions were analyzed and classified using the principal component analysis. The AE signals corresponding to surface cracking showed higher in peak amplitude and peak frequency, and shorter in rise time than those corresponding to moisture movement. To reduce the multicollinearity among AE features and to extract the significant AE parameters, correlation analysis was performed. Over 99% of the variance of AE parameters could be accounted for by the first to the fourth principal components. The classification feasibility and success rate were investigated in terms of two statistical classifiers having six independent variables (AE parameters) and six principal components. As a result, the statistical classifier having AE parameters showed the success rate of 70.0%. The statistical classifier having principal components showed the success rate of 87.5% which was considerably than that of the statistical classifier having AE parameters.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05c
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• pp.170-176
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• 2002

In this papers, we proposed the feature extraction method due to partial discharge type of transformers. For wavelet transform, Daubechie's filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion, skewness, kurtosis) about acoustic emission signal generated from each partial discharge type. The defects which could occur in a transformer were simulated by using needle-plane electrode, IEC electrode and Void electrode. Also, these coefficients are used to identify signal of partial discharge type electrode fault in transformer. As a result, from compare of acoustic emission amplitude and acoustic average value, we are obtained results of IEC electrode> Void electrode> Needle-Plane electrode. otherwise, In case of skewness and kurtosis, we are obtained results of Needle-Plane electrode electrode> Void electrode> IEC electrode.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.1
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• pp.84-92
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• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.