• 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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• 제13권9호
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• pp.31-37
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• 1996

This Paper is concerned with effective signal identification method for tool breakage and micro chipping using discrete wavelet transform of feed motor current in milling operations. The wavelet transform uses an analyzing waveletfunction which is localized in both frequency and time domain to detect subtle time localized changes in input signals. The changing pattern of wavelet coefficient is continuously compared to detect tool breakage and micro chipping over one spindle revolution. The results indicate that the wavelet transform can identify tool failure with much greater sensi- tivity than the time domain monitoring and frequency domain monitoring such as FFT. Experimental results are presented to support the proposed scheme.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.40-45
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• 1999

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc.In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA (parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.

• 한국생산제조학회지
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• 제6권3호
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• pp.36-43
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• 1997

In order to make unmanned machining systems with satisfactory performances, it is necessary to incorporate appropriate condition monitoring systems in the machining workstations to provide the required intelligence of the expert. This paper deals with condition monitoring for tool wear and breakage during turning operation. Developing economic sensing and identification methods for turning processes, sound pressure measurement and digital signal processing technique are proposed. The validity of the proposed system is confirmed through the large number of cutting tests.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 춘계학술대회 논문요약집
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• pp.177-177
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• 2004

최근 높은 복잡한 형상의 고정밀도와 고성능을 갖는 제품들을 생산할 수 있게 하기 위하여 고속 가공기의 공구 셋팅 모니터링 시스템의 사용은 고속 가공기를 이용하여 생산하는 여러 제품 공정에 절대적으로 요구되고 있다. 가공물의 형상이 고정밀화 되어가고 있으며 이들의 가공 정밀도와 생산성을 향상시키고 공구들의 파손을 방지하고 공구의 셋팅 시간을 단축하는 것이 매우 중요하다.(중략)

• 대한기계학회논문집A
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• 제25권6호
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• pp.972-979
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• 2001

Tool fracture index(TFI) was developed in order not only to detect tool fracture but also to predict the amount of tool fracture in face milling. TFI is calculated by using peak-to-valley values of cutting force acting on teeth and their ratio between the adjacent teeth. When the tool fractures, a large value of TFI proportional to the amount of tool fracture was obtained periodically and decreased gradually. It was found that TFI is independent of cutter runout and it almost does not vary during transient cutting such as cutting condition change during machining. The threshold of tool fracture can be analytically determined by TFI developed in this paper, because the magnitude of TFI was shown to be dependent on the ratio of the amount of tool fracture to feed per tooth and immersion ratio. It was possible to predict the amount of tool fracture in experiments by using the proposed TFI.

• 대한기계학회논문집A
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• 제24권4호
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• pp.900-906
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• 2000

Ceramic, PCD and CBN tools are available for the difficult-to-cut-materials such as hardened carbon tool steel, stainless steel, Inconel 718 and etc. Ceramic tools are likely to be chipped and abruptly broken before the appearance of normal wear in turning. Ceramic tools are suitable for continuous in turning, not for intermittent in milling. In this study, TiN/TiCN multi-layer coated ceramic tools were found to restrain the chipping, breaking and early fracture and to increase the critical cutting speed owing to TiN/TiCN multi-layer coating in Arc Ion Plating of PVD method.

• 한국생산제조학회지
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• 제9권5호
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• pp.144-149
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• 2000

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc. In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.

• 한국정밀공학회지
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• 제12권6호
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• pp.112-119
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• 1995

Early fracture and cutting force of ceramic tool for hardened STC3 steel was investigated in this study. It was found that early fracture of ceramic tool was mostly occurred before normal wear was progressed beyond a critical cutting speed and normal wear was performed under the critical cutting speed. The relationships among critical cutting speed, which was a cause of early fracture, suggested cutting cross section, that is, maximum thickness of cut and width of cut, and cutting force were examined. The following conclusions were obtained: (1)Critical cutting speed showed a high value in the case of small maximum thickness of cut and large nose radius, but was not influenced by width of cut, (2)Principal, feed and radial force, respectively, showed the proportional value to constant cutting area, width of cut and maximum thickness of cut orderly, (3)Occurrence of early fracture was dependent upon radial force.

• 융합신호처리학회논문지
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• 제23권2호
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• pp.84-90
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• 2022

공작기계 상태 진단은 기계의 상태를 자동으로 감지하는 프로세스이다. 실제로 가공의 효율과 제조공정에서 제품의 품질은 공구 상태에 영향을 받으며 마모 및 파손된 공구는 공정 성능에 보다 심각한 문제를 일으키고 제품의 품질 저하를 일으킬 수 있다. 따라서 적절한 시기에 공구가 교체될 수 있도록 공구 마모 진행 및 공정 중 파손 방지 시스템 개발이 필요하다. 본 논문에서는 공구의 적절한 교체 시기 등을 진단하기 위해 딥러닝 기반의 계층적 컨볼루션 신경망을 이용하여 5가지 공구 상태를 진단하는 방법을 제안한다. 기계가 공작물을 절삭할 때 발생하는 1차원 음향 신호를 주파수 기반의 전력스펙트럼밀도 2차원 영상으로 변환하여 컨볼루션 신경망의 입력으로 사용한다. 학습 모델은 계층적 3단계를 거쳐 5가지 공구 상태를 진단한다. 제안한 방법은 기존의 방법과 비교하여 높은 정확도를 보였고, 실시간 연동을 통해 다양한 공작기계를 모니터링할 수 있는 스마트팩토리 고장 진단 시스템에 활용할 수 있을 것이다.