• 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)

• 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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.5
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.126-130
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• 1993

소경드릴가공은 많은 기계가가공중에서도 가장 곤란한 가공의 하나이다.그것은 가공구멍단면 이하의 공간속에서 공구강성이나 칩처리들이 고려되어야 한다는 엄격한 제한이 소경이란 형태에서 한층 어려워지기 때문이다.소경의 구멍가공은 최근 전자제품,우주항공기 부품,소형정밀부품, 섬유산업의 광섬유관련품 등에 까지 수요가 증가함에 따라 레이져가공,전자빔가공,전해가공과 같은 전기물리적가공법이 많이 사용되고 있지만 생산성 및 가공정밀도의 관점에서 만족스러운 결과를 얻을 수 없는 실정이다, 이에반해 기계가공인 소경드릴가공은 공구강성저하로 인해 쉽게 파손된다는 점은 있지만 가공정도가 양호하고 종횡비가 높은 가공이 가능하여 실용화가 가장 좋은 분야라고 할수 있다. 이로 인해 최근에는 여기에 관한 많은 연구가 지행되고 있다. 또한 기계가공의 자동화가 진전됨에 따라서 단일공국의 대표적 공구인 바이트의 결함을 검출하는것 못지않게 드릴의 마멸이나 절손의 검출 또는 예측이 중요한 문제로 부각됨에 따라 절삭저항의 이용이 증가할 것으로 생각된다. 따라서 본 연구에서는 ADI에 포함된 Si량이 드릴가공시 ADI의 피삭성에 미치는 영향을 절삭조건을 변화시켜 고찰함과 동시에 공구수명에 대하여 고찰하였다.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.1
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• pp.27-37
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• 1993

In unmanned machining, One of the most essential issue is the tool management system which includes controlling. identification, presetting and monitoring of cutting tools. Especially the monitoring of tool wear and fracture may be the heart of the system. In this study a computer vision based tool monitoring system is developed. Also an algorithm which can determine the tool condition using this system is presented. In order to enhance practical adaptability the vision system through which two modes of images are taken is located over the rake face of a tool insert. And they are analysed quantitatively and qualitatively with image processing technique. In fact the morphologies of tool fracture or wear are occurred so variously that it is difficult to predict them. For the purpose of this problem the pattern recognition is introduced to classify the modes of the tool such as fracture, crater, chipping and flank wear. The experimental results performed in the CNC turning machine have proved the effectiveness of the proposed system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.9-14
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• 1994

The modern CNC machine require increasingly an exact monitoring and control of cutting process. They are to make final taret which construct full automation factories as unmanned system. In this study, we decided that we develop new techique to monitor and detect tool breakage on the machining operation using face milling machine with multi-point throwaway tips. The technology in which the tool is illuminated by an beam of Laser is used by image of tool fracture through CCD camera.

• 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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.89-94
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• 1993

최근들어 공작기계의 급속한 발전은 절삭작업의 자동화와 무인화를 가능하게 만들었으며 이에따라 절삭가공의 완전한 무인화를 실현하기 이해서는 절삭가공중 발생하는 각종 이상 상태를 in-process로 감시하고 검출하는것이 매우 중요하게 되었다. 이상상태는 절삭공구의 마모나 파손, 채터진동의 발생, 절삭가공에 방해를 주는 절삭칩등을 들수 있으며 이 같은 현상을 검출하기 위한 많은 연구가 활발히 진행되고 있다. 본 연구에서는 내식성,내마모성,내열성 및 기계적 성질이 우수하거나 절삭시 가공 경화성이 크고, 열 전도성이 불량하며, 공구재료와 응착이 쉬어 난색재로 알려지고 톱니형 연속칩이 주로 발생하는 STS304를 선택하여 절삭실험을 하였다. 절삭 조건에 따른 칩 형태를 관찰하여, 절삭조건과 절삭력을 이용하여 칩의 형태를 분류하였으며, 절삭가공중에 칩형태를 검출 할수 있는 가능성에 대하여 연구 하였다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.1
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• pp.11-18
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• 2010

Tool condition monitoring (TCM) is crucial for improvement of productivity in manufacturing process. However, TCM techniques have not been applied to monitor tool failure in an industrial gear shaving application. Therefore, this work studied a statistical TCM method for monitoring gear shaving tool condition. The method modeled the vibration signal of the shaving process using beta probability distribution in order to extract the effective features for TCM. Modeling includes rectifying for converting a bi-modal distribution into a unimodal distribution, estimating the parameters of beta probability distribution based on method of moments. The performance of features obtained from the proposed method was evaluated and discussed.