• 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.

• Tribology and Lubricants
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• v.28 no.3
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• pp.130-135
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• 2012

This study presents a new methodology for realtime tool breakage detection by sensor fusion concept of two hall sensor and an acoustic emission (AE) sensor. Spindle induction motor torque of CNC Lathe during machining is estimated by two hall sensor. Estimated motor torque instead of a tool dynamometer was used to measure the cutting torque and tool breakage detection. A burst of AE signal was used as a triggering signal to inspect the cutting torque. A significant drop of cutting torque was utilized to detect tool breakage. The algorithm was implemented on a NI DAQ (Data Acquisition) board for in-process tool breakage detection. The result of experiment showed an excellent monitoring capability of the proposed tool breakage detection system. This system is available tool breakage monitoring through internet also provides this system's user with current cutting torque of induction motor.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.3
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• pp.36-43
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• 1997

In order to make unmanned machining systems with satisfactory performances, it is necessary to incorporate appropriate condition monitoring systems in the machining workstations to provide the required intelligence of the expert. This paper deals with condition monitoring for tool wear and breakage during turning operation. Developing economic sensing and identification methods for turning processes, sound pressure measurement and digital signal processing technique are proposed. The validity of the proposed system is confirmed through the large number of cutting tests.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.38-43
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• 1994

In milling process, monitoring and diagosis system is very importent to accomplish factory automation. In this study, to drvelope on-line tool breakage detection system in face milling operation, analysis and experiment were performed. The tool breakage detection experiment was performed in machining center and the effectiveness of the detection tool breakage detection alorithm and the usage of feed drive current as a detection signal were verified.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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• pp.31-37
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• 1996

This Paper is concerned with effective signal identification method for tool breakage and micro chipping using discrete wavelet transform of feed motor current in milling operations. The wavelet transform uses an analyzing waveletfunction which is localized in both frequency and time domain to detect subtle time localized changes in input signals. The changing pattern of wavelet coefficient is continuously compared to detect tool breakage and micro chipping over one spindle revolution. The results indicate that the wavelet transform can identify tool failure with much greater sensi- tivity than the time domain monitoring and frequency domain monitoring such as FFT. Experimental results are presented to support the proposed scheme.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.2
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• pp.95-103
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• 1993

In the manufacturing field, some traditional manufacturing and machining methods become weakened the productivity, the external competitive power, and accuracies of the products. In these point of view, the unmanned and intelligent manufacturing systems are proposed by some manufacturing companies. The real-time monitoring technology of the cutting tool conditions i.e. tool wear, tool breakage, crack, and chipping anre necessarily reauired to realize those system, especially. In this study, we constructed the multi- sensing system using the acceleration sensor, the current sensor, and the loadmeter of a machine tool. Also, we analyzed the nose breakage, the massive signal, and some monitoring features by means of the developed system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.315-318
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• 1997

In terms of productivity, the speed of machining process has been increasing in most of engineering part. But the tapping process does not reach at enough level compared with other machining processes because of its complicate cutting mechanism. In the high speed tapping process, the one of important elements is tool monitoring system to prevent tool breakage. This paper describes tool monitoring system by acoustic emission(AE) in the tapping process. We used 2 types of AE sensors in this test. The one is commercial sensor which is used in other machining monitoring system like polishing and the other is a self-fabricated sensor for this test. In this test we purpose to find out the frequency of AE signal in tapping process and verify the possibility of applying AE sensor in in-process tapping monitoring system. Also grasp of characteristic of tapping process by AE signal is handled.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.2
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• pp.23-34
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• 1995

This paper is concerned with effective and reliable tool breakage detection method using pattern characteristics of feed motor current in milling operations. Correlation coefficient is derived from the feature vector of signal for two consecutive which are extracted feed motor current over three spindle revolutions. The changing pattern of correlation coefficient is continuously compared to detect tool breakage and monitor cutting conditions. This proposed monitoring scheme is not affected by different tools, friction of motion, and varying cutting conditions and material shapes. Experimental results are presented to support the proposed monitoring scheme.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.98-107
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• 1995

The in-process detection of drill wear and breakage is one of the most importnat technical problems in unmaned machining system. In this paper, the monitoring system is developed to monitor abnormal drilling states such as drill breakage, drill wear and unstable cutting using motor current. Drill breakage is detected by level monitoring. Tool wear is classified by fuzzy pattern recognition. The key feature for classification of tool wear is the estimated flank wear which is calculated by the proposed flank wear model. The characteristic of the model is not sensitive to the variation of cutting conditions but is sensitive to drill wear state. Unstable cutting states due to the unsmooth chip disposal and the overload are monitored by the variance/mean ratio of spindle motor current. Variance/mean ratio also includes the information about the prediction of drill wear and drill breakage. The evaluation experiments have shown that the developed system works very well.