• 한국정밀공학회지
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• 제12권3호
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• pp.92-100
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• 1995

This experimental study is mainly investigated on the mean cutting forces and AE(acoustic emission) parameters in order to detect and estimate the tool failure in the pachinig of SUS304 by face milling Mean cutting forces and AE parameters can detect the tool failure in face milling. Effective detection parameters are AE RMS, AE energy, AE count, AE duration, and z-direction mean cutting force. From the analysis of cutting tool failure detection, the tool failure of face milling is caused by sudden increasing of the cutting force.

• 한국생산제조학회지
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• 제5권3호
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• pp.73-79
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• 1996

In this paper present a new technique (strain-telemetering)for detection of coated tool failure in face milling processes. In the cutter body the strain signals received fro the transmitter is transformed in to frequency modulation(FM) signals in face milling processes. A receiver which is place near by the Vertical milling machine receives the FM signals, then the signals will be sent to a computer which determines whether th tool is failure. And machined surface of workpiece is detected by the SEM. In this paper, A on-line monitoring of the tool failure detection system based on the strain -telemetering apparatus has bee represented.

• 한국정밀공학회지
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• 제13권11호
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• pp.100-105
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• 1996

In this paper presents a new technique (stain-telemetering) for detection of coated tool failure in face milling processes. In the cutter body, the strain signals received from the transmitter are transformed into frequency modulation(FM) signals in face milling processes. The receive which is placed near by the Vertical milling machine receives the FM signals, then the signals are sent to a computer, which shows the tool failure. In this paper, A on-line monitoring of the tool failure detection system based on the strain-telemetering apparatus has been represented.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 춘계학술대회 논문집
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• pp.1063-1066
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• 2002

The wavelet transform is a popular tool for studying intermittent and localized phenomena in signals. In this study the wavelet transform of cutting force signals was conducted for the detection of a tool failure in turning process. We used the Daubechies wavelet analyzing function to detect a sudden change in cutting signal level. A preliminary stepped workpiece which had intentionally a hard condition was cut by the inserted cermet tool and a tool dynamometer obtained cutting force signals. From the results of the wavelet transform, the obtained signals were divided into approximation terms and detailed terms. At tool failure, the approximation signals were suddenly increased and the detailed signals were extremely oscillated just before tool failure.

• 한국정밀공학회지
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• 제19권12호
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• pp.78-85
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• 2002

The wavelet transform is a popular tool fer studying intermittent and localized phenomena in signals. In this study the wavelet transform of cutting force signals was conducted for the detection of a tool failure in turning process. We used the Daubechies wavelet analyzing function to detect a sudden change in cutting signal level. A preliminary stepped workpiece which had intentionally a hard condition was cut by the inserted cermet tool and a tool dynamometer obtained cutting force signals. From the results of the wavelet transform, the obtained signals were divided into approximation terms and detailed terms. At tool failure, the approximation signals were suddenly increased and the detailed signals were extremely oscillated just before tool failure.

• 한국기계가공학회지
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• 제9권5호
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• pp.1-6
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• 2010

The wavelet transform is a popular tool for studying intermittent and localized phenomena in signals. In this study the wavelet transform of cutting force signals was conducted for the detection of a tool failure in turning process. We used the Daubechies wavelet analyzing function to detect a sudden change in cutting signal level. A preliminary stepped workpiece which had intentionally a hard condition was cut by the inserted cermet tool and a tool dynamometer obtained cutting force signals. From the results of the wavelet transform, the obtained signals were divided into approximation terms and detailed terms. At tool failure, the approximation signals were suddenly increased and the detailed signals were extremely oscillated just before tool failure.

• 한국공작기계학회논문집
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• 제14권6호
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• pp.16-21
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• 2005

The cutting characteristics of hardened steel(AISI 52100) by PCBN tools is investigated with respect to cutting force, workpiece surface roughness and tool flank wear by the vision system. Hard Owning is carried out with various cutting conditions; spindle rotational speed, depth of cut and feed rate. Backpropagation neural networks(BPNs) are used for detection of tool wear. The input vectors of neural network comprise of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output is the tool wear state which is either usable or failure. The detection of the abnormal states using BPNs achieves $96.8\%$ reliability even when the spindle rotational speed and feedrate are changed.

• 한국안전학회지
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• 제33권3호
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• pp.83-91
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• 2018

This paper suggests a weibull time delay model to evaluate failure risks in FMEA(failure modes and effects analysis). Assuming three types of loss functions for delayed time in failure cause detection, the risk of each failure cause is evaluated as its occurring frequency and expected loss. Since the closed form solution of the risk metric cannot be obtained, a statistical computer software R program is used for numerical calculation. When the occurrence and detection times have a common shape parameter, though, some simple results of mathematical derivation are also available. As an enormous quantity of field data becomes available under recent progress of data acquisition system, the proposed risk metric will provide a more practical and reasonable tool for evaluating the risks of failure causes in FMEA.

• 한국기계연구소 소보
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• 통권19호
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• pp.61-68
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• 1989

As the system monitoring technology become required in order to improve the system performance and the productivity, we’ve studied to the detection for the tool wear and the tool breakage using AE sensors that is able to detection of generated high frequency stress pulse at cutting. The detection system is consist of a sensing part, a amplifier part, a signal processing part, and a analysis & output part. The moment (a rms and a kurtosis) of statistical method is used for analysis of AE singnal. The experiment are carried out in a CNC lathe. In this study, we achieved that the amplitude level of the AE signal and statistical moments was largely changed as the tool failure. The change rate of Kurtosis was especially large, but the change rate of the rms was small.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 1993년도 제18회 학술대회 초록집
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• pp.77-84
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• 1993

Systems with journal bearings generally operate in large scale and under severe loading conditions such as steam generator turbines and internal combustion engines, in contrast to the machineries using rolling element bearings. Failure of the bearings in these machineries can result in the system breakdown. To avoid the time consuming repair and considerable economic loss, the detection of incipient failure in journal bearings becomes very important. 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. It has been known that the intervention of foreign materials, insufficient lubrication and misassembly etc. are principal factors to cause bearing failure and distress. The experiment was conducted under such designed conditions as inserting alumina particles to the lubrication layer in the simulated journal bearing system. The results showed that acoustic emission could be an effective tool to detect the incipient failure in journal bearings.