• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.344-348
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• 1992

This paper presents a new methodology for on-line tool breakage detection by sensor fusion concept of an acoustic-emission (AE) sensor. A built-in piezoelectric force sensor was used to measure cutting force instead of a tool dynamometer to preserve the machine tool dynamics. he sensor was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. When a tool is broken, the explicit changes of signals' pattern take place. A burst-type AE signal increases abruptly. Followingly, a cutting force drops significantly. Therefore a burst of AE signal is used as a triggering signal to inspect the following cutting force. Significant drop of cutting force is utilized to detect tool breakage. The algorithm was implemented in a DSP board for in-process tool breakage detection. The proposed monitoring system was capable of a good applicable tool breakage detection.