• 한국정밀공학회지
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• 제15권5호
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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.

• 한국정밀공학회지
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• 제15권8호
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• pp.116-125
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• 1998

In this paper, main target is to select parameters for prediction of tool wear and detection of tool fracture. The research about choosing parameter for prediction of tool wear is done by using force ratios. Also current sensor, tool-dynamometer, and accelerometer are used for researching detection method of tool fracture. Experiment is done using Taguchi's method in medium machining conditions. Parameter which is best for prediction of tool wear and detection of tool fracture by deviation analysis is selected. In this paper, tool wear means flank wear.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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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.

• 오토저널
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• 제8권3호
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• pp.28-36
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• 1986

This study was investigated the feasibility of AE application on in-process detection of tool fracture and chipping. Carbon steel SM45C workpiece with longitudinal slots was turned interruptedly on a lathe. AE RMS signal at tool fracture was observed and also the tangential force and the feed observed at the time of tool fracture, the levels of tangential force and the feed force at the time of fracture decrease considerably. In chipping, high level AE signal was observed but there were no changes of cutting force. Peak AE RMS squared is proportional to the area of tool fracture and resultant force. Fracture model of tool fracture is proposed as $V_{p}$ = $C_{1}$ $E_{1}$F(.DELTA. A)$_{0.5}$ and peak AE RMS shows strong correlation with the fracture parameter F(.DELTA.A)$^{0.5}$.

• 한국정밀공학회지
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• 제15권5호
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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.

• 한국정밀공학회지
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• 제12권4호
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• pp.67-77
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• 1995

In modern industry the requirement of automation of manufacturing process increases so that unmanned system has been popular as an ultimate goal of modern manufacturing process. In unmanned manufacturing process the tool fracture is a very serious problem because it results in the damage of workpieces and can stop the operation of whole manufa- turing system. In this study, image processing technique is used to detect the fracture of insert tip of face milling using multi-point-tool. In order to acquire the image information of fracture shape of rotation insert tip. We set up the optical system using a light beam chopper. In this system we can reduce the image degradation generated from stopped image of rotating insert tip using image restoration technique. We calculated the mean square error to diagnose the condition of tool fracture, and determind the criteria of tool fracture using experimental and staticstical method. From the results of this study we've developed non- contact detection technique of tool fracture using image processing method and proposed the fracture direction of automation and unmanned system considering the optimal time of tool change milling.

• 대한기계학회논문집
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• 제16권4호
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• pp.707-722
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• 1992

본 연구에서는 공구의 큰 파손에서 부터 공구의 기하학적 형태가 크게 변하지 않는 작은 파손등 파손의 크기와 파손부위에 따른 신호의 형태를 조사하고자 한다. 그 방법으로서 가공재료로서는 SM45C강(steel)과 열처리한 SM45C강을 이용하였다. 공구파손에 따른 AE신호와 절삭력신호의 변화를 분석하기 위하여, AE신호의 경우는 RMS값을 택하고 그 진폭의 갑작스런 변화와 평균 레벨의 변화를 이용하여 파손의 순간 을 감지하였고, 절삭력의 경우에는 절삭력의 주분력, 이송분력 그리고 배분력의 변화 형태를 이용하여 공구의 이상상태를 알 수 있었으며, 이 두 신호의 변화를 동시에 관 찰함으로써 임의의 가공조건에서 공구의 파손을 감지할 수 있는 알고리즘 개발의 기준 을 마련할 수 있었다.

• 한국생산제조학회지
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• 제8권2호
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• pp.100-107
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• 1999

End milling experiments are conducted to investigate characteristics of laser beam signals due to tool fracture. The laser beam signals are obtained with adapt focusing of tool. Tool states are identified wit h scanning electron microscopy and optical microscopy. It is demonstrated that the laser beam signals provide reliable informations about the cutting processes and tool states. Moreover, tool fracture can be detected successfully using coefficient of variation.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.26-31
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• 1994

The analysis of acoustic emission signals generated during machining has been proposed as a technique for studying both the fundamentals of the cutting process and process and as a methodology for detecting tool fracture on line. In this study, AE signals detected during End Milling were applied as the experimental test to sensing tool fracture on the CNC vertical milling machine. Because automatic monitoring of the cutting condition is one of the most important technologies in machining, the in-process detection of cutting tool life including fracture has been investigated by performing experimental test.

• 비파괴검사학회지
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• 제11권1호
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• pp.31-37
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• 1991

Acoustic emission(AE) signals detected during metal cutting were applied as the experimental test to sensing tool wear and chipping on the NC vertical milling machine. The in-process detection of cutting tool wear including chipping, cracking and fracture has been investigated by means of AE in spite of vibration or noise through intermittent metal cutting, then the following results were obtained 1) When the tool wear is increased suddenly, or the amplitude of AE signals changes largely, it indicates chipping or breaking of the insert tip. 2) It was confirmed that AE signal is highly sensitive to the cutting speed and tool wear. 3) At the early period of cutting, the wear were large and RMS value increased highly by the influence of minute chipping and cracking, etc. Therefore, the above situations should be considered for the time when the tool would be changed.