• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.315-318
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• 1997

In terms of productivity, the speed of machining process has been increasing in most of engineering part. But the tapping process does not reach at enough level compared with other machining processes because of its complicate cutting mechanism. In the high speed tapping process, the one of important elements is tool monitoring system to prevent tool breakage. This paper describes tool monitoring system by acoustic emission(AE) in the tapping process. We used 2 types of AE sensors in this test. The one is commercial sensor which is used in other machining monitoring system like polishing and the other is a self-fabricated sensor for this test. In this test we purpose to find out the frequency of AE signal in tapping process and verify the possibility of applying AE sensor in in-process tapping monitoring system. Also grasp of characteristic of tapping process by AE signal is handled.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.4
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• pp.61-67
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• 1999

In turning the chip may be produced in the form of continuous chip or discontinuous one. Continuous chips produced at high speed machining may hit the newly cut workpiece surface and adversely affect the appearance of the surface finish and may interfere with tool and sometimes induce tool fracture. In this study relationship between AE signal and chip form was experimentally investigated, The experimental results show that types of chip form are possible to be classified from the AE signal using fuzzy logic.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1572-1579
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• 2006

An usual acoustic emission(AE) event has two widely characterized parameters in time domain, peak amplitude and event duration. But noise in AE measuring may disturb the signals with its parameters and aggrandize the signal incertitude. Experiment activity of detection of the nick inside of porcelain with AE was made and study on AE signal processing with statistic be presented in this paper in order to pick-up information expected from the signal with noise. Effort is concentrated on developing a novel arithmetic to improve extraction of the characteristic from stochastic signal and to enhance the voracity of detection. The main purpose discussed in this paper is to treat with signals on amplitudes with statistic mutuality and power density spectrum in frequency domain, and farther more to select samples for neural networks training by means of least-squares algorithm between real measuring signal and deterministic signals under laboratory condition. By seeking optimization with the algorithm, the parameters representing characteristic of the porcelain object are selected, while the stochastic interfere be weakened, then study for detection on neural networks is developed based on processing above.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.307-311
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• 1993

Research concerning gears included in rotating machines has been reported using the acoustic emission (AE) method, however, almost no research has been conducted using the AE method in regard to running gears in a bending fatigue processor spur gear teeth. Therefore, in this report, a power circulating-type gear testing machine was used and AE signals and crack length were measured in the bending fatigue process of case-hardened spur gear. Furthermore, the envelope of the AE signal was detected and various analysis were carried out in this data. In the course of the experiments, the following results were observed : the AE signal envelope consists mainly of contact frequency component and twice as many as this;two peaks of AE appear in each tooth contact by the tip corner contact ; as a result of the severe tip corner contact ; as a result of the severe tip corner contact with the sudden increase of crack length, AE signal becomes large.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.3
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• pp.76-82
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• 1999

This study was carried out to verify finding performance of dental diamond bur and investigate the possibility of AE application in density field. Work pieces were made of acryl and bovine respectively for the experiments in this study. Grinding test was conducted to get the data of grinding resistance and specific finding energy of low different types of diamond bur by using tool dynamometer. AE signal was acquired to verify grinding process in the AE measuring system. AErms value was increased as the grinding velocity and depth were increasing, but it decreased as the feed rate was increasing. The case of the small value of AE signal is due to abnormal grinding in D type diamond bur. By analyzing AErms start and finish time of grinding working, abnormal grinding state can be confined. Abnormal state can be found through the behavior of AE signal in the finding working. As a result, it is expected that forecast of abnormal state is possible using AE equipments under real time process.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.3
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• pp.14-17
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• 2006

To diagnosis abnormal compressor conditions in an air-conditioner, the acoustic emission (AE) signal, which is derived from wear condition, compressed air, and assembly error, was analyzed experimentally. Burst and continuous type AE signals resulted from metal contact and compressed air, and the raw AE signal of compressors was acquired in the production line. After extracting samples using waveforms, the Early Life Test (ELT) was conducted and the waveform was classified as normal or abnormal. Efficient parameters in the waveform pattern were investigated in time and frequency domains and a diagnosis algorithm for air-conditioners using Neural Network estimation is suggested.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.334-339
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• 2004

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmilling is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for state monitoring is also presented in the paper.

• KSTLE International Journal
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• v.1 no.1
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• pp.29-33
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• 2000

In this study, a methodology is developed and confirmed to find the physical contact between the slider and disc due to the defects of disk during head seeking operation using acoustic emission (AE) signal. The head/disk contact was detected during random and standard seeks, whereas no contact was detected during track fellowing. During standard and random seeks, the torsion mode of slider excitation was observed at 680KHz. Therefore, it is thought that AE technique can be used as an alternative method of the glide test by monitoring existence of the torsional mode of the slider during seek operation or can be used to detect the spacing loss during seeking operation. By appropriately choosing the location of the sensor an order of magnitude increase in the sensitivity for RMS AE signal is observed. Therefore we can find take-off velocity clearly with high signal to noise ratio of AE signal.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.10
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• pp.1454-1459
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• 2012

An improved detection of radiation-induced paramagnetic faults was developed to identify the irradiation status of basil and clove. The effectiveness of different sample pretreatments, including freeze-drying (FD), oven-drying (OD), alcoholic-extraction (AE), and water-washing and alcoholic-extraction (WAE), were examined. All non-irradiated samples showed a single central signal ($g_0$=2.006), whereas radicals representing two additional side peaks ($g_1$=2.023 and $g_2$=1.986) with a mutual distance of 6 mT were detected in the irradiated samples. AE and WAE produced the best results for irradiated clove in terms of intensities of radiation-specific ESR signals and their ratios to the central signal. However, FD provided the highest intensities of radiation-specific ESR signals for basil, whereas their ratios to the major signal were better in the cases of AE and WAE. Signal noise, particularly due to $Mn^{2+}$ signals, was observed, whereas it decreased in AE and WAE pretreatments. Based on our results, AE and WAE can improve the detection conditions for radiation-specific ESR signals in irradiated samples.

• Journal of the Korean Society for Railway
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• v.4 no.3
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• pp.79-86
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• 2001

The signal pattern recognition method by acoustic emission signal is applied to detect and classify the defects of a journal bearing in a power plant. AE signals of main defects such as overheating, wear and corrosion are obtained from a small scale model. To detect and classify the defects, AE signal pattern recognition program is developed. As the classification methods, the wavelet transformation analysis, the frequency domain analysis and time domain analysis are used. Among three analyses, the wavelet transformation analysis is most effective to detect and classify the defects of the journal bearing..