• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.16 no.5
• /
• pp.90-95
• /
• 2007

Single side final polishing is a very important role to stabilize a wafer finally before the device process on the wafer is executed. In this study, the machining variables, such as pressure, machining time, and the velocity of pad table were adopted. These parameters have the major influence on the characteristics of wafer polishing. We investigated the surface roughness changing these variables to find the optimal polishing condition. Pad, slurry, slurry quantity, and oscillation distance were set to the fixed variables. In order to reduce defects and find a stable machining condition, a hall sensor was used on the polishing process. AE sensor was attached to the polishing machine to verify optimal condition. Applying data analysis of the sensor signal, experiments were performed. We can get better surface roughness from loading the quasi static force and improving wafer-holding method.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.23 no.4
• /
• pp.325-329
• /
• 2014

This paper proposes a method to predict chatter vibration generated in milling processes and to enhance machining quality and surface finish. Chatter vibration is a common problem in the milling of thin walls and floors. It causes a poor surface finish, or even marks, to appear on the final machined surface. Therefore, an effective method is necessary to predict chatter vibration in machine tools. In this investigation, chatter vibration is measured by an accelerometer, microphone, and Acoustic Emission (AE) sensor in a machining operation. Based on the results of the experiment, a microphone can be applied for the prediction of chatter vibration in milling processes.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2012.10a
• /
• pp.388-393
• /
• 2012

Vibration condition monitoring at low rotational speeds is still a challenge. Acoustic emission (AE) is the most used technique when dealing with low speed bearings. At low rotational speeds, the energy induced from surface contact between raceway and rolling elements is very weak and sometimes buried by interference frequencies. This kind of issue is difficult to solve using vibration monitoring. Therefore some researchers utilize artificial damage on inner race or outer race to simplify the case. This paper presents vibration signal analysis of low speed slewing bearings running at a low rotational speed of 15 rpm. The natural damage data from industrial practice is used. The fault frequencies of bearings are difficult to identify using a power spectrum. Therefore the relatively improved method of empirical mode decomposition (EMD), ensemble EMD (EEMD) is employed. The result is can detect the fault frequencies when the FFT fail to do it.

• Journal of Mechanical Science and Technology
• /
• v.18 no.7
• /
• pp.1055-1061
• /
• 2004

In this research, a vision system for detecting breakages of small-diameter taps, which are rarely detected by the indirect in-process monitoring methods such as acoustic emission, cutting torque and motor current, was developed. Two HMI (Human Machine Interface) programs to embed the developed vision system into a Siemens open architecture controller, 840D, were developed. They are placed in sub-windows of the main window of the 840D and can be activated or deactivated either by a softkey on the operating panel or the M code in the NC part program. In the event that any type of tool breakage is detected, the HMI program issues a command for an automatic tool change or sends an alarm signal to the NC kernel. An evaluation test in a high-speed tapping machine showed that the developed vision system was successful in detecting breakages of small-diameter taps up to M1.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2000.05a
• /
• pp.5-12
• /
• 2000

본 연구에서, Daubechies'Mother Wavelet를 이용한 이산 웨이블렛 변환(Discrete Wavelet Transform)에 기초한 새롭고 효과적인 특정추출방법을 제안하였다. 특정추출을 이용하여 응용방향을 설명하고 또는 통계적 파라메터의 평가를 행하였다. 본 연구에서는 다음과 같은 몇 가지 사실을 알 수 있었다. 1. 시스템에서 발생된 (인가전압이 0[V]) 노이즈라 볼 수 가있는 렌덤노이즈(Random Noise)를 디지털필터인 FIR(Finite Impulse Response)필터를 통하여 상당한 노이즈를 억제할 수가 있었다. 2. 이산 웨이블렛 변환 시 레벨 1~4까지 변환한 결과 최적의 변환상태 Level-3을 기준으로 하였다. 3. 특정추출 파라메터는 음향방출신호의 최대값, 평균값, 분산, 왜도, 첨쇄도를 특정추출파라메터로 이용하였다. 4. 특정추출 결과를 이용하여 전체 열화시간 중 대표적 음향방출신호 중 초기열화신호, 중기열화신호, 말기열화신호를 얻을 수 있었다. 이런 특정추출을 통하여 변압기열화상태를 진단할 수 있는 가능성을 확인 할 수가 있었다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2004.07a
• /
• pp.515-518
• /
• 2004

The Piezoelectric ceramics for AE(Acoustic Emission) sensor are desired large electromechanical coupling factor, high mechanical quality factor and good characteristic resonance frequency. In this study, the empirical formula of specimens is used $0.9Pb(Zr_xTi_{1-x})O_3-0.1Pb(Mn_{1/3}Nb_{1/3}Sb_{1/3})O_3$ (PZT-PMNS). The piezoelectric and dielectric characteristic are investigated by sintering temperature and value of x as functions of $Ti^{2+},\;Zi^{2+}$ mol rate. MPB(morphotropic Phase boundary) is defined in the x=0.522. Because it is appeared to the best piezoelectric and dielectric characteristic in the x=0.522, it can be application by AE sensor. PZT-PMNS ceramics without pre-amplifier and filter are tested for detecting of arc signal. The detection characteristic is evaluated wave form, frequency distribution.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2006.06a
• /
• pp.505-506
• /
• 2006

PD(partial discharge) occurred from variable PD sources in air may be the cause of breakdown in high voltage equipment which affect huge outage in power system. Identification and localization of PD sources is very important for engineer to cope with huge accident beforhand. PD phenomena can be detected by acoustic emission sensor or electromagnetic sensor like antenna. This paper has investigated the identification method using PCA(principal component analysis) for the PD signals from variable PD sources, for which the electric field distribution and PD inception voltages were simulated by using commercial FEM program. PD signals was detected by ultra wideband antenna. Their own features were extracted as the frequency coefficients transformed with FFT(fast fourier transform) and used to obtain independent pincipal components of each PD signals.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.2
• /
• pp.78-83
• /
• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.3
• /
• pp.121-126
• /
• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.14 no.2
• /
• pp.122-131
• /
• 1994

Journal bearings in the rotating machineries are vulnerable to the contamination or the insufficient supply of lubricating oil, which is likely to be the cause of unexpected shutdown or malfunction of these systems. Various destructive and nondestructive testing methods had been used for the reduction of maintenance cost and the operational safety problems due to the accidents related to bearing damages. In this experimental approach, acoustic emission monitoring is employed to the detection of incipient failure caused by intervention of foreign particles most probable in the journal bearing systems. Experimental schedules for the intervention of foreign particles was composed to be more quantitative and systematic than last study in consideration of minimum oil film thickness and particle size. The experiment was conducted under such designed conditions as inserting alumina particles to the lubrication layer in the simulated journal bearing system. Several parameters such as AE rms level, waveform, AE energy distribution and other AE event parameters are used for analysis and characterization of damage source. The results showed that the history of damage was well correlated with the changes of AE rms level and the type of damage source signal can be verified using other informations such as waveform, distributions of AE parameters etc.