• Journal of the Korean Society for Precision Engineering
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• v.15 no.8
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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.

• Journal of the Korean Society for Nondestructive Testing
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• v.11 no.1
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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.

• 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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• 2000.11a
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• pp.888-891
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• 2000

Drilling is one of the most important operations in machining industry and usually the most efficient and economical method of cutting a hole in metal. From automobile parts to aircraft components, almost every manufactured product requires that holes are to be drilled for the purpose of assembly, creation of fluid passages, and so on. It is therefore desirable to monitor drill wear and hole quality changes during the hole drilling process. One important aspect in controlling the drilling process is drill wear status monitoring. With the monitoring, we may decide on optimal timing for tool change. The necessity of the detection of tool wear, fracture and the abnormal tool state has been emphasized in the machining process. Accordingly, this paper deals with the cutting characteristics of the hot-rolled high strength steels using common HSS drill. The performance variables include drill wear data obtained from drilling experiments conducted on the workpiece. The results are obtained from monitoring of the cutting force and Acoustic Emission (AE) signals, and from the detection of the abnormal tool state with the computer vision system.

• Journal of the Korean Society for Precision Engineering
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• v.7 no.3
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• pp.75-82
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• 1990

Automatic monitoring of the cutting conditions is one of the most improtant technologies in machining. In this study, the feasibility in applying acoustic emission(AE) signals for the in-process detection of endmill wear and fracture has been investigated by performing experimental test on the NC vertical milling machine with SM45C for specimen. As the results of detecting and analyzing AE signals on various cutting conditions, the followings have confirmed. (1) The RMS value of acoustic emission is related sensitively to the cutting velocity, but is not affected largely by feed rate. (2) The burst type AE signals of high level have been observed when removing chips distorderly and discontinuously. (3) When the RMS value grows up rapidly due to the increase of wear the endmill are generally broken or fractured, but when the endmills fracture at the conditions of smooth chip-flow or built-up-edge(BUE) occurred frequently, the rapid change of the RMS arenot found. And it is expected that this technigue will be quite useful for in-process sensing of tool wear and fracture.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.245-250
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• 2002

A successful on-line monitoring system for conventional machining operations has the potential to reduce cost, guarantee consistency of product quality, improve productivity and provide a safer environment for the operator. In fee-shape machining, typical signs of tool problems such as vibration, noise, chip flow characteristics and visual signs are almost unnoticeable without the use of special equipment. These characteristics increase the importance of automatic monitoring in fine-shape machining; however, sensing and interpretation of signals are more complex. In addition, the shafts of the micro-tools break before the typical extensive cutting edge of the tool gets damaged. In this study, the existence of a relationship between the characteristics of the cutting force and tool usage was investigated, and tool breakage detection algorithm was developed and the fellowing results are obtained. In data analysis, didn't use a relative error compare which mainly used in established experiment and investigated tool breakage detection algorithm in time domain which can detect AE and cutting force signals more effective and accurate.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.1
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• pp.28-37
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• 2004

An on-line monitoring system of endmill failure such as weal, chipping, and fracture is developed using AE, cutting force Characteristic variations of AE and cutting force signals due to endmill failure are identified as follows. When endmill fracture occurs, AE count rate shows a rapid Increase in conjunction with a subsequent decrease while a standard deviation of the principal cutting force Increases significantly. The increase of AE count rate precedes the Increase of standard deviation of principal cutting force. Chipping results in relatively small increase and decrease of AE count rate without any significant variation of the cutting force Gradual increase of AE count rate and mean principal cutting force are Identified to be related with the wear of cutter. A cutter fracture detection algorithm is developed based on the present results. The signals me normalized to enhance the applicability of the algorithm to Wide those of fresh cutters, and qualitative characteristics of AE signals encountered at the moment of fracture are employed. It is demonstrated that the algorithm can detect the cutter fracture successfully.