• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.982-986
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• 1997

A method using cutting force signal and neural network for detection tool damage is proposed. Cutting force signal is gained by tool dynamometer and the signal is prepocessed to normalize. Cutting force signal is changed by tool state. When tool damage is occurred, cutting force signal goes up in comparison with that in normal state. However,the signal goes down in case of catastrophic fracture. These features are memorized in neural network through nomalizing couse. A new nomalizing method is introduced in this paper. Fist, cutting forces are sumed up except data smaller than threshold value, which is the cutting force during non-cutting action. After then, the average value is found by dividing by the number of data. With backpropagation training process, the neural network memorizes the feature difference of cutting force signal between with and without tool damage. As a result, the cutting force can be used in monitoring the condition of cutting tool and neural network can be used to classify the cutting force signal with and without tool damage.

• Annals of Clinical Neurophysiology
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• v.1 no.1
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• pp.70-75
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• 1999

Trancranial Doppler(TCD) monitoring is a new application of ultrasonography which allows the nonivasive detection of blood flow velocity in the horizontal (M1) segment of the middle cerebral artery (MCA) and detects microembolic phenomena in the cerebral circulation. Recent studies emphasized the potential of using this technique in vascular surgery (carotid endarterectomy, cardiopulmonary bypass), interventional and intensive care setting. Although the disparity between CBF and blood flow velocity and number of microemboli could be used to prevent cerebral ischemic and embolism based on clinical studies. A reduction of more than 60% of MCA can reflex hemodynamic ischemic state and acoustic feedback of high intensity transient signals(HITS) from the TCD monitoring unit has a direct influence on surgical technique. TCD monitoring can immediately provide information about thromboembolism and hemodynamic changes, which may be a useful tool in the study and prevention of stroke.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.61-66
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• 1994

Tool condition monitoring is one of the most important aspects to improve productivity and quality and to achieve intelligent machining system. The tool state is classified into three groups as chipping, wear and fracture. In this study, wear of a ceramic cutting tool for hardened die material (SKD11) was investigated. Flank wear was occured more dominant than crarer wear. Therefore, to predict flank wear, the modeling of cutting force has been performed. The modeling of cutting force by an assumption that act the stress distribution on the tool face obtained through a numerical analysis. The relationships between the cutting force and the tool wear can be constructed by machining paraneters with cutting conditions. Experiments were performed under the various cutting conditions to ensure the validity of force models. The theoretical predictions of the flank wear is approximately in good agreement with experimental result.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.108-114
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• 2007

A new cutting force estimating approach and machining state monitoring examples are presented which uses a cylindrical displacement sensor built into the spindle. To identify the tool-spindle system dynamics with frequency up to 2 kHz, a home-built electro-magnetic exciter is used. The result is used to build an algorithm to extract the dynamic cutting force signal from the spindle error motion; because the built-in spindle sensor signal contains both spindle-tool dynamics and tool-workpiece interactions. This sensor is very sensitive and can measure broadband signal without affecting the system dynamics. The main characteristic is that it is designed so that the measurement is irrelevant to the geometric errors by covering the entire circumferential area between the target and sensor. It is also very simple to be installed. Usually the spindle front cover part is copied and replaced with a new one with this sensor added. It gives valuable information about the operating condition of the spindle at any time. It can be used to monitor cutting force and chatter vibration, to predict roughness and to compensate the form error by overriding spindle speed or feed rate. This approach is particularly useful in monitoring a high speed machining process.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.57-63
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• 2022

This study describes a system that monitors the tool and cutting state of automatic beveling operation in real time. As a signal for cutting state monitoring, a motor current detected from the spindle drive system of the automatic beveling machine is used to monitor abnormal state. Because automatic beveling is processed using a face milling cutter, the cutting force mechanism is the same as the milling process. The predicted cutting torque is obtained using a cutting force model based on specific cutting resistance. Then, the predicted cutting torque is converted into the spindle motor current value, and cutting state stability is diagnosed by comparing it with the motor current value detected during beveling operation. The experimental results show that the spindle motor current can detect abnormal cutting state such as overload and tool wear during beveling operation, and can diagnose the cutting stability using the proposed equip-current line diagram.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.1
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• pp.65-71
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• 2003

In a high speed spindle system it is very important to monitor the state of rotating rotor. Particularly in active control spindle system the position sensor must provide feedback to the control system on the exact position of the rotor. In order to monitor the state of a high speed spindle exactly, high accuracy and wide frequency bandwidth of sensors are important. The focus in this paper is to make a fiber optic sensor for monitoring rotor of magnetic bearing, to design the circuit for detecting optical signal, and to evaluation static and dynamic characteristics of fiber optic sensor.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.393-398
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• 2004

This paper is focused on practical applicability of the optical fiber sensor considering the machine center which is going to use them. Optical fibers may be fluctuated because the machine center operates as column moving type. This causes distortion of the sensor output signal. To reduce this problem, we have improved the sensor structure and its bracket. And we evaluated performances of the sensor.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.411-416
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• 2002

In a high speed spindle system, it is very important to monitor the state of rotating rotor. Particularly in active control spindle system, the position sensor must provide feedback to the control system on the exact position of the rotor. In order to monitor the state of a high speed spindle exactly, high accuracy and wide frequency bandwidth of sensors are important. The focus in this paper is to make a fiber optic sensor for monitoring rotor of magnetic bearing, to design the circuit for detecting optical signal, and to evaluation static and dynamic characteristics of fiber optic sensor.

• Transactions of Materials Processing
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• v.31 no.1
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• pp.5-10
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• 2022

In multi-stage cold forging process, to enhance the productivity and product quality, in-site process monitoring technique by implanting sensors such as piezo-sensor and acoustic emission sensor has been continuously studied. For accurate analysis of the process, the selection of appropriate sensors and implantation positions are very important. Until now, in a multi-state forging machine, wedge parts located at the end of punch-set are used but it is difficult to analyze minute changes in die block-set. In this study, we also implanted sensors to the die part (die spacer) and compared signals from both sensors and found that sensing signals from die part showed enhanced process monitoring results.