• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.04a
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• pp.438-442
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• 1994

절삭가공에서 공구의 마멸은 생산의 최적화에 영향을 주는 가장 중요한 요소중의 하나라고할 수 있다. 따라서 생산시스템이 자동화되고 유연성 및 생산성이 증대되면서, 공구의 수명이 끝났을 때의 공구교환을 위한 최적 의사 결정전략(Decision making stratagy)은 그 중요성이 점차 커지고 있다. 한편, 공구는 마멸의 진행에 따라 그 수명을 예측하여 교환해 주는 것이 바람직하다. 그러나 공구의 마멸은 여러가지 요인들의 복합적 작용에의해 발생하는 현상 이므로 그것을 정확히 예측한다는 것은 많은 어려움이 있다. 본 연구에서는 절삭력을 이용하여 공구의 여유면마멸 (Flank wear)과 경사면마멸을 감지하고자 한다 먼저 절삭력을 정적인 성분(Static component)과 동적인 성분(Dynamic component)로 구분하여 공구의 마멸을 감지하는데 이용하였다. 절삭력의 정적인 성분은 절삭조건의 변화에 대해 정규 화된(Normalized) 절삭력으로 모델링하여 공구의 여유면 마멸을 감지 하였다. 또한 공구의 경사면마멸이 발생한 경우에는 절삭력의 동적인 성분이 크게 변화함을 알 수 있었다.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.252-252
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• 2004

새로운 공작기계나 절삭공구의 설계 및 개선을 위하여 절삭 공정 중 발생되는 절삭력 성분을 정확히 예측하는 것이 필요하다. 절삭 과정에서 절삭력 정보의 중요성은 그동안 공작기계 분야에서 익히 강조되어 왔다. 특히 주 절삭력 정보는 공구 파손을 예측하고 마모를 감지하여 그 밖의 다른 오동작을 검출해 내는 것에 있어서 매우 중요한 것으로 잘 알려져 있다. 최근 공작기계 강성 및 성능의 향상, 고속절삭용 공구의 발전, 금형 산업의 생산성과 정밀도 향상의 요구로 머시닝센터를 중심으로 고속가공에 관한 연구가 활발히 진행되고 있다. (중략)

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.1
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• pp.1-9
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• 2001

The development of flexible automation in the manufacturing industry is concerned with production activities performed by unmanned machining system A major topic relevant to metal-cutting operations is monitoring toll wear, which affects process efficiency and product quality, and implementing automatic toll replacements. In this paper, the measurement of the cutting force components has been found to provide a method for an in-process detection of tool wear. The static com-ponents of cutting force have been used to detect flank wear. To eliminate the influence of variations in cutting conditions, tools, and workpiece materials, the force modeling is performed for various cutting conditions. The normalized force dis-parities are defined in this paper, and the relationships between normalized disparity and flank were are established. Final-ly, artificial neural network is used to learn these relationships and detect tool wear. According to proposed method, the static force components could provide the effective means to detect flank wear for varying cutting conditions in turning operation.

• 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 Machine Tool Engineers Conference
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• 2002.10a
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• pp.141-146
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• 2002

Real-time monitoring and control of the cutting force is essential for unmanned cutting process. Although the cutting force can be measured directly using tool dynamometers, their implementation is not feasible in industry due to high cost. Alternative approach is the cutting force estimation based on spindle drive models, but it requires the knowledge of their characteristics with the spindle speed variation. This paper investigates the power-factor and frictional characteristics of three-phase induction motors and determines its characteristics below and above the base speed, respectively. In order to realize the proposed cutting force monitoring system, a stand-alone DSP board was utilized. Its monitoring and control performance is evaluated in a CNC lathe.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.48-55
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• 1997

A useful method to detect tool breakage suing neural network of cutting force signal is porposed and implemented in a basic cutting process. Cutting signal is gathered by tool dynamometer and normalized as a preprocessing. The cutting force signal level is continually monitored and compared with the predefined level. The neural network has been trained normalized sample data of the normal operation and cata-strophic tool failure using backpropagation learning process. The develop[ed system is verified to be very effective in real-time usage with minor modification in conventional cutting processes.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.3
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• pp.150-159
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• 2000

In-process diagnosis of the cutting state is essential for the automation of manufacturing systems. Especially when the cutting process becomes unstable it induces self-exited vibrations a frequent case of poor tool life rough surface finish damage to the workpiece and the machine tool itself and excessive down time. To ensure that the cutting process main-tains stable it is highly desirable to have the capability of real-time. To ensure that the cutting process main-tains stable it is highly desirable to have the capability of real-time monitoring and controlling chatter. This paper describes the detection method of chatter vibration using cutting force in turning process. In order to detect a chatter vibra-tion the dynamic fluctuation of radial force is analyzed since this components is sensitive to the chatter. The envelope sig-nal of radial force has been calculated by the use of FIR Hilbert transformer and it was useful to classify the chatter signal from the dynamically unstable circumstances. It was found that the mode and the mode width were closely correlated with the chatter amplitude was well. Finally back propagation(BP) neural network have been applied to the pattern recognition for the classification of chatter signal in various cutting conditions. The validity of this systed was confirmed by the experiments under the various cutting conditions.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.219-225
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• 1999

Monitoring of the cutting force signals in cutting process has been well emphasized in machine tool communities. Although the cutting force can be directly measured by a tool dynamometer, this method is not always feasible because of high cost and limitations in setup. In this paper an indirect cutting force monitoring system is developed so that the cutting force in turning process is estimated based on a AC spindle drive model. This monitoring system considers the cutting force as a disturbance input to the spindle drive and estimates the cutting force based on the inverse dynamic model. The inverse dynamic model represents the dynamic relation between the cutting force, the motor torque and the motor power. The proposed monitoring system is realized on a CNC lathe and its estimation performance is evaluated experimentally.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1991.04a
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• pp.328-333
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• 1991

가공 프로세스의 감시에 대한 많은 연구는 주로 과부하, 공구파손, 공구마멸, 채터링, 충돌 승을 대상으로 한 것으로, AE(Acoustic Emission), 모터전류, 절삭력, 진동, 절삭온 도 등의 신호를 감지한 뒤 이들 신호에 각종 신호 처리를 행하여 가공 상태에 대한 정보를 추출하고, 그것에 근거한 감시를 하고 있다. 작업중 감시연구에서 주로 제기 되는 문제는 공구교환이나 공작물 교환에 무관하게 또 작업에 지장을 주지 않으면서 이들 신호를 감지해야 하는 점과 감시처리의 신뢰성을 높여야 하는 점이다. 본 연구에서는 NC프로그램으로 부터 얻은 예측정보를 이용해서 선삭가공에서 감시 시스템의 성능향상을 시도 하였다. 예측정보는 감시코드의 형태로 각 NC블럭에 추가하여 실시간 감시에서 작업의 상태를 인식할 때 참고 정보로 활용하기 때문에 감시에 대한 신뢰성을 높일 수 있었다. 감시신호로는 이송축 직류 서보 모터의 전류를 사용하였다. 전체적인 감시 시스템의 프로그래밍 언어로는 C 를 사용하여, 실시간 감시처리를 가능하게 하였다.