• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.3
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• pp.7-15
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• 2018

Drilling processes require a cutting monitoring function that can be analyzed and gives feedback about strange conditions, tool collision and tool wear in real time. In this study, we proposed a drill monitor using the torque from the main spindle in CNC machine tools and a PROFIBUS network as a PLC-based interface. This paper studied drilling torque changes depending on drill size, the repetition cutting of the drilling and the drill's wear in the same cutting conditions. The material of the drills was high speed steel (HSS) and uncoated. The drills chosen were 2.7 mm, 6.7 mm, and 10.0 mm in diameter. These drills were selected because they had basic holes for their taps.

• Journal of Advanced Marine Engineering and Technology
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• v.18 no.4
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• pp.62-68
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• 1994

Recently, the trends toward reduction in size and weight of industrial products increased the application of micro hole for manufacturing gadgets of high precision and gave rise to a great deal of interest for micro hole drilling M/C. Quite a few research work is performed on micro drilling on domestic basis compared with the tendency of analyzing cutting mechanism, adaptive control, monitoring of generally available drills of diameter greater than 1mm. This study adresses the design, manufacturing and controlling a micro hole drilling M/C with the overload detection instrument and the step feed mechanism. Controlling and monitoring of the drilling process are acomplished on PC basis for more user interfaces and effectiveness. The test machine of the results of this research shows a good foundation for extending further micro hole machining technique.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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• pp.133-140
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• 1999

In this study, a monitoring method to detect chip disposal state in drilling system based on neural network was proposed and its performance was evaluated. If chip flow is bad during drilling, not only the static component but also the fluctuation of dynamic component of drilling. Drilling torque is indirectly measured by sensing spindle motor power through a AC spindle motor drive system. Spindle motor power being measured drilling, four quantities such as variance/mean, mean absolute deviation, gradient, event count were calculated as feature vectors and then presented to the neural network to make a decision on chip disposal state. The selected features are sensitive to the change of chip disposal state but comparatively insensitive to the change of drilling condition. The 3 layerd neural network with error back propagation algorithm has been used. Experimental results show that the proposed monitoring system can successfully recognize the chip disposal state over a wide range of drilling condition even though it is trained under a certain drilling condition.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.5
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• pp.1-6
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• 2010

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.