• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.10a
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• pp.239-243
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• 2001

본 연구에서는 절삭중 공구파손을 실시간으로 검출할 수있는 시스템을 구성하기 위한 일환으로 일차로 AE(acoustic emission)센서를 사용하여 mechanism의 특성과 공구파손에 따른 주파수 특성을 해석했고, 차후 연구로서 AE센서가 아닌 가속도센서와 전류센서 및 공작기계의 절삭동력계를 이용한 복합계측시 스템을 구성하여 절삭특성 및 공구파손이 발생할 때의 신호특성을 해석했다.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.4
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• pp.43-53
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• 2016

In this paper, monitoring method of tool fracture using motor current was proposed for turning process. In order to take more reliable current signal, cutting force signal was compared as reference signal because cutting force signal is reliable, and analysis of signal correlation between cutting force and motor current was performed. The static components of the cutting force and motor current signals were correlated very well for different cutting conditions, and it was proven to use the motor current as an proper sensor for monitoring of tool fracture. To understand the characteristics of motor current, various kinds of cutting experiment were performed including tool fracture experiments. As a result, a new method to detect tool fracture using motor current in turing was proposed, and a large number of fracture experiments were carried out to evaluate the reliability of the proposed method. Finally, it can be possible to detect the tool fracture reliably.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.4
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• pp.707-722
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• 1992

Fracture of a cutting tool is one of the most serious problems in machining systems. Therefore, several methods have been proposed so far to detect cutting tool fracture. However, most of them have some problems from the viewpoint of practical applications. In this study, the feasibility of using acoustic emission and cutting force signals for the detection of massive tool breakages as well as small fracture of cutting tools were investigated. Turning experiments were performed using conventional carbide inset tools under realistic cutting conditions and the SM45C steel and heat treated SM45C steel were used as a workpiece. And the sensitivities of the AE and cutting force signals to the fracture of cutting tools were illustrated. Finally, a detection algortithm for the fracture of cutting tools was developed through the analysis of these dual signals in the several types of tool fracture.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.881-888
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• 1997

A new algorithm for detection of tool fracture in milling process was developed. The variation of the peak-to-valley value of cutting load was used in this algorithm. Various kinds of vectors representing the condition of tool, such as tool condition vector, reference tool condition vector, tool condition variation vector were defined. Using these vectors, tool fracture index which represents the magnitude of tool fracture and is independent of tool run-outs is developed. Small and large tool fracture and chipping under various cutting condition could be detected using proposed tool fracture index, which was proved with cutting force model and experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.64-69
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• 1993

최근의 생산시스템은 FMS,FMC와 같은 고도로 자동화된 무인시스템으로 운용되고 있으며, 생산성 향상을 위한 무인운전의 필요성이 증대되고 있으나, 숙련된 작업자를 대신하여 작업상태를 감시하는 신뢰성 있는 감시 시스템의 부족으로 인해 곤란을 겪고 있다.따라서 작업자를 대신할수 있는 신뢰성있는 감시 시스템의 개발을 필요로 한다. 특히 공구파손,공구마멸과 같은 공구손상은 공작물 및 기계에 치명적 손상을 초래하고, 기계정지시간을 증가시키므로 공구파손 검출과 공구마멸의 실시간 센싱은 가공 프로세스의 자동화와 신뢰성을 증가시키는데 가장 중요한 역활을 수행한다. 본 연구에서는 드릴가공시 검출한 주축 및 Z축 모터전류를 기초로 하여 드릴마멸을 추정하는 모델을 개발하고자 한다.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.5
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• pp.100-109
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• 1998

In order to increase productivity through unmanned machining in CNC milling process, in-process tool fracture detection is required. In this paper, a new algorithm for tool fracture detection using cutting load variations was developed. For this purpose, developed were tool condition vector which is dimensionless indicator of cutting load and tool fracture index (TFI) which represents magnitude of tool fracture. Through cutting force simulation, tool fracture index was shown to be independent of tool run-outs and cutting condition variations. Using tool fracture index, the ratio of the tool fracture to feed per tooth could be indentified.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.5
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• pp.110-119
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• 1998

Dynamic cutting force variations in milling process were measured indirectly using spindle motor current. Magnitude of the spindle motor current is independent of cutting direction. Quasi-static sensitivity of the spindle motor current is higher than that of the feed motor current. Dynamic sensitivity of the spindle motor current is lower but cutting force was correctly represented by spindle RMS current in rough milling. In rough milling, chipping and tool fracture were well detected by the proposed tool fracture index using spindle motor current.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.76-78
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• 1993

간접적인 방법으로 가공중(In process)공구상태를 감시하기 위해, 센서신호를 분석하는 방법으로 시간영역 (Time Domain) 해석과 주파수 영역(Frequency Domain)해석이 주로 이용되어 왔다. 시간영역해석의 경우 RMS,PEak Value, 평균/분산을 이용한 정적분석과 AR 모델, ARMA 모델, Kalman Filter등 동적 시계열 모델이 연구되어 왔다. 주파수영역해석의 경우 푸리에 변환 (Fourier Transform)에 의한 신호해석 기술이 주로 이용되고 있다. 그러나 푸리에 변환된 결과에는 시간정보가 포함되어 있지 않고, 국부적인 변환결과가 전체를 대표하는 성질을 가지고 있다. 이에 비해 웨이브렛(Wavelet) 변환은 고주파성분에 대해서는 시간분해능이 높고, 저주파 성분에 대해서는 주파수분해능이 높은 다중해상도 해석기술로서 국소적인 변동점을 민검하게 검지하는 것이 가능하다. 본연구에서는 엔드밀 가공중 발생하는 공구의 파손을 검출하기 위해, 전류센서로 부터 얻은 이송축 부하 전류의 변화에 웨이브렛 변환을 통해 공구의 파손을 검출하는 방법에 대한 연구결과를 소개한다.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.6
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• pp.1-6
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• 2015

Tool condition monitoring plays one of the most important roles in the improvement of both machining quality and productivity. In this regard, various process signals and monitoring methods have been developed. However, most of the existing studies used cutting force or acoustic emission signals, which posed risks of interference with the machining system in dynamics, fixturing, and machining configuration. In this study, a feed motor current signal is used as a process signal representing process and tool states in tool breakage monitoring based on an adaptive autoregressive model and unsupervised neural network. From the experimental results using various cases of tool breakage, it is shown that the developed system can successfully detect tool breakage before two revolutions of the spindle after tool breakage.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.208-208
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• 2004

공작기계와 절삭 공구의 발달로 고속 가공(high speed machining)의 도입이 가속화되고 있다. 고속 가공은 소재 제거율(MRR; material removal rate)을 향상시킴으로써 생산비용 및 생산 시간의 단축이 가능하며, 소경 공구를 이용한 고속 고회전 가공으로 고정밀 가공이 가능한 방법으로 생산 효율성 및 정밀성의 증대를 동시에 추구할 수 있는 가공 방법이다. 고속 가공에서 공구의 절입 및 퇴출 시에 급격한 절삭력의 변화로 인하여 공구의 파손(breakout) 및 칩핑(chipping)의 발생 가능성이 보다 높다.(중략)