• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.155-160
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• 1993

연삭가공은 숫돌의 입자가 마멸,파쇄,탈락,생성의 과정을 반복하면서 가공하는 것으로 연삭과정은 사용하는 연삭숫돌의 종류, 드레싱조건,연삭조건 등의 인자에 영행을 받는다. 더욱이 연삭숫돌의 연삭성능은 연삭가공시간의 경과에 따라 변화한다. 이때 요구되는 가공능률과 가공정밀도를 일정하게 유지하기 위해서는 연삭과정을 자동감시하고 이상상태를 진단하는 기술의 확립이 필수적이다. 본 연구에서는 AE를 이용하여 평면연삭에 있어서 연삭숫돌의 종류별(WA계 비트리파이드 및 레지노이드결하ㅂ제연삭숫돌 36종류) 및 연삭조건을 변화시켰을때의 연삭저항 및 AE 신호의 변화등을 In-process 검출하여 연삭가공상태의 자동감시 및 자동이상진단시스템을 위한 AE의 적용 가능성을 검토하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.921-924
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• 2004

This paper is to investigate a crack for plasma sprayed MCrAlY coated material by acoustic emission method in 4-point bending test. The CoNiCrAlY is coated on Inconel-718 by vacuum plasma spray process. Micro-hardness measurement was conducted by means of Micro Vickers-hardness indentor. The porosity of coating layer was measured using a SEM and Image Analyzer. AE monitoring system is composed of PICO type sensor, a wide band preamplifier(40dB), a PC and AE DSP(16/32 PAC) board. The AE count, Hit and energy of coating specimens is measured according to coating thickness.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.155-159
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• 1993

최근 많은 생산 시스템의 자동화에 있어서 기계의 상태 진단 및 감시는 설비의 중요도 및 특수성를 고려할때 매우 중요한 비중을 차지하게 되며, 생산 작업을 최적화할 수 있는적당한 제어기술의 필요성과 그에 대한 관심이 날로 증가 하고있는 실정이다. 특히 가공분야에서 많은 부분을 차지하고 있는 절삭가공작업은 기구의 구성이 복잡하고 불확정한 요인을 포함하고있으며 공구의 파손이나 채터진동에 의한 공작물의 정도의 변화가 급속히 발생하기 때문에 이를 위하여 인프로세서 감시가 절실히 요구되고 있다. 그러므로 비정상적인 절삭을 사전에 감지하여 대처함으로써 최적의 작업조 건하에서 안정된 절삭을 할 수 있고 공작기계의 유지, 보수에 경제적인 절감을 기대할 수 있다. 본 연구에서는 2차원 절삭과정중에 발생하는 채터진도에 있어서 절삭 파라메타와 AE 신호와의 관계를 실험적으로 규명하며, AE를 이용한 절삭과정을 모니터링 할 수 있는 방법에 대하여 연구한다.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.118-123
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• 2004

The compressor has one of important roles in refrigeration cycle and it determines refrigeration efficiency and quality This paper aims to monitor rotary compressors for room air conditioners by using Acoustic Emission(AE) technique. The reliability of rotary compressors has been evaluated through visual inspection on them after long term test. This paper describes methods for acquisition and processing AE raw signal to monitor the state of rotary compressor. A detecting method of abnormal compressor in real time is suggested and special-purpose monitoring system which can be applied to automatic manufacturing line is developed using one-chip microprocessor at low cost.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.938-942
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• 2008

To detect the failures in machine, the signals of current, vibration and acoustic emission are widely used in industry. And unexpected failures of gears are not only extremely damaging but also lead to economic losses. In this paper, to detect the misalignment occurred at between two gears in gearboxes, the signals of current, vibration and AE were measured at gearbox and motor power line. FFT(Fast Fourier Transform) was used for current and vibration signal analysis to find gear failure frequency. Especially, the envelop analysis and wavelet transform were applied for AE signal. Therefore, compared with the results of three kinds of signal, the possibility of earily detection by AE is identified or checked.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.115-119
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• 1996

An estimation of surface roughness value is a very important and difficult issue in grinding process. The definition of the D.A.R.F(Dimensionless Average Roughness Factor) has been made including the absolute average and tile standard deviation that are the parameters of the AE(Acoustic Emission) sign. The theoretical equation of the surface roughness applying the D.A.R.F has been derived from the regressive analysis and specified with respect to the availability through the experimental approach on the machine.

• Journal of the Korean Society for Nondestructive Testing
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• v.10 no.2
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• pp.23-31
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• 1990

Acoustic Emission(AE) signals obtained during fracture toughness test and fatigue test for nuclear pressure vessel material(SA 508 cl.3) and artificial AE signals from pencil break and ultrasonic pulser were classified using pattern recognition methods. Three different classifiers ; namely Minimum Distance Classifier, Linear Discriminant Classifier and Maximum Likelihood Classifier were used for pattern recognition. In this study, the performance of each classifier was compared. The discrimination of AE signals from cracking and crack surface rubbing was possible and the analysis for crack propagation was applicable by pattern recognition methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.975-979
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• 2002

This paper studies the indirect parameters when the new and worn end mill working in the machining center. The parameter output methods are cutting force, current values and AE signals. In the result, when the worn end mill operating, cutting forces increase the 14.71〔N〕, current values increase the 2.917〔A〕 and 1.168〔A〕 according to the spindle mote. and feed motor, and AE signals increase the 0.588$\times$10$^{-5}$ 〔A〕. We can use these parameters in the detection of end mill wear.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.9
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• pp.53-57
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• 2009

There are technical requirements to manufacture large size functional parts with not only simple geometries like a flat or spherical surface but also sculptured geometries. In addition, the required machining accuracy for these parts is becoming more severe. In general, the form accuracy of machined parts is determined by the relative position between workpiece and tool during machining process. To improve machining accuracy the relative position errors should be maintained within the required accuracy. This study deals with the estimation and calibration of depth of cut using the AE signal in micro-machining. Also, this sensing technique can be applied to detect the initial contact between workpiece and tool.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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• pp.57-63
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• 1996

Monitoring of grinding troble occurring during the process is classified into the quantitative data which depends upon a sensor and the qualitative knowledge which relies upon an empirical knowledge. Since grinding operation is highly related with a large amount of functional parameters, it is actually deficulty in copying wiht the grinding troubles through the process. To cope with grinding trouble, it is an effective monitoring systems when occurring the grinding process. The use of neural networks is an effective method of detection and/or monitroing on the grinding trouble. In this paper, four parameters which are derived from the AE(Acoustic Emission) signatures are identified, and grinding monitoring system utilized a back propagation learning algorithm of PDP neural networks is presented.