• Title/Summary/Keyword: tool failure detection

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An Experimental Study on the Tool Failure Detection in the Machining by Face Milling (정면밀링 가공시 발생하는 공구파손 검출에 관한 실험적 연구)

  • Seo, Jae-Hyung;Kim, Seong-Il;Kim, Tae-Young
    • Journal of the Korean Society for Precision Engineering
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    • v.12 no.3
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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.

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An Experimental Study onthe Detection of Tool Failure I Face Milling Processes (정면밀링가공시 공구 파손 검출에 관한 실험적 연구)

  • 김우순
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.5 no.3
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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.

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An Experimental Study on the Detection of Tool Failure Using Telemetering Technique (텔레미터링기법을 이용한 공구 파손 검출에 관한 실험적 연구)

  • Kim, W.S.;Lee, J.H.;Kim, D.H.
    • Journal of the Korean Society for Precision Engineering
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    • v.13 no.11
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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.

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Detection of Tool Failure by Wavelet Transform (Wavelet 변환을 이용한 공구파손 검출)

  • Yang, J.Y.;Ha, M.K.;Koo, Y.;Yoon, M.C.;Kwak, J.S.;Jung, J.S.
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2002.05a
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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.

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Analysis and Denoising of Cutting Force Using Wavelet Transform (Wavelet 변환을 이용한 절삭신호 분석과 노이즈 제거)

  • 하만경;곽재섭;진인태;김병탁;양재용
    • Journal of the Korean Society for Precision Engineering
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    • v.19 no.12
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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.

A Study on Micro ED-Drilling of cemented carbide (초경합금의 미세방전 드릴링에 관한 연구)

  • Kim, Chang-Ho;Kang, Soo-Ho
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.9 no.5
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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.

A Study on the Cutting Characteristics and Detection of the Abnormal Tool State in Hard Turning (고경도강 선삭 시 절삭특성 및 공구 이상상태 검출에 관한 연구)

  • Kim Tae Young;Shin Hyung Gon;Lee Sang Jin;Lee Han Gyo
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.14 no.6
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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.

Risk Evaluation of Failure Cause for FMEA under a Weibull Time Delay Model (와이블 지연시간 모형 하에서의 FMEA를 위한 고장원인의 위험평가)

  • Kwon, Hyuck Moo;Lee, Min Koo;Hong, Sung Hoon
    • Journal of the Korean Society of Safety
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    • v.33 no.3
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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.

A Study of Tool Breakage Dection Using AE Sensor (AE(acoustic emission)센서를 이용한 공구파손검출에 관한 연구)

  • Lee, Jae-Jong;Song, Jun-Yeop;Park, Hwa-Yeong
    • 한국기계연구소 소보
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    • s.19
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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.

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Monitoring of Lubrication Conditions in Journal Bearing by Acoustic Emission (AE를 이용한 저어널 베어링에서의 윤활유 이물질 혼입의 영향 감시)

  • 윤동진;권요양;정민화;김경웅
    • Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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    • 1993.12a
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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.

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