• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.6
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• pp.140-145
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• 2007

To improve productivity of a metal cutting process, it is required to monitor machining stability in real time. Since cutting environment is harsh against sensing conditions due to vibration, chip, and cutting fluid, etc., it is necessary to develop a robust and reliable sensing system for the practical application. In this work, a chatter monitoring system was developed and its effectiveness was proved. Spindle displacement caused by cutting was selected as a main monitoring parameter. A cylindrical capacitive displacement sensor was adopted. Chatter frequencies were identified through modal analysis. To quantify chatter vibrations, chatter correlation coefficient was introduced. The identification of the monitoring system showed a good agreement with the result of experiment.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.09a
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• pp.121-125
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• 2005

In this paper, the web-based monitoring system is developed for the process monitoring of mold manufacturing. The cutting force is measured by hall-sensors which is low cost and useful to be installed in machine tool indirectly. Specially, the current of main spindle in machine tool is converted into cutting force by various experiments. For effective emote monitoring, the interface that is able to offer the information of current process and cutting signal to client is establish.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.25-32
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• 1995

In highly automated milling process, in-process monitoring of the malfunction is indispensable to ensure efficient cutting operation. Among many malfunctions in milling process, chatter vibration deteriorates surface finish, tool life and productivity. In this study, the monitoring system of chatter vibration for face milling process is proposed and experimentally estimated. The monitoring system employs two types of sensor such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are extracted in time domain for the input patterns of neural network to reduce time delay in signal processing state. The resultes of experimental evaluation show that the system works well over a wide range of cutting conditions.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.120-129
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• 1995

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.6
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• pp.972-979
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• 2001

Tool fracture index(TFI) was developed in order not only to detect tool fracture but also to predict the amount of tool fracture in face milling. TFI is calculated by using peak-to-valley values of cutting force acting on teeth and their ratio between the adjacent teeth. When the tool fractures, a large value of TFI proportional to the amount of tool fracture was obtained periodically and decreased gradually. It was found that TFI is independent of cutter runout and it almost does not vary during transient cutting such as cutting condition change during machining. The threshold of tool fracture can be analytically determined by TFI developed in this paper, because the magnitude of TFI was shown to be dependent on the ratio of the amount of tool fracture to feed per tooth and immersion ratio. It was possible to predict the amount of tool fracture in experiments by using the proposed TFI.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.109-117
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• 2005

In case of fine machining processes, the cutting state monitoring by a skilled operator is impossible because the physical changes generated during fine machining are very weak. To realize the high efficient and precise fine machining, it is necessary to develop the sensor based monitoring system which is able to detect the fine changes of cutting state. In this paper, the fine acoustic emission monitoring system is developed to monitor the state of the fine machining process. The developed system consists of the AE sensor and the AE signal processing unit. And this has the high-sensitivity and bandwidth which can detect fine AE signal generated during fine machining process. In order to investigate the feasibility of the developed system, evaluation experiments were performed in the fine fixed-abrasive machining processes such as polishing and glass ferrule slicing. Experimental results show that the developed monitoring system possesses an excellent real-time monitoring capability at fine machining processes.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.4
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• pp.707-722
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• 1992

Fracture of a cutting tool is one of the most serious problems in machining systems. Therefore, several methods have been proposed so far to detect cutting tool fracture. However, most of them have some problems from the viewpoint of practical applications. In this study, the feasibility of using acoustic emission and cutting force signals for the detection of massive tool breakages as well as small fracture of cutting tools were investigated. Turning experiments were performed using conventional carbide inset tools under realistic cutting conditions and the SM45C steel and heat treated SM45C steel were used as a workpiece. And the sensitivities of the AE and cutting force signals to the fracture of cutting tools were illustrated. Finally, a detection algortithm for the fracture of cutting tools was developed through the analysis of these dual signals in the several types of tool fracture.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.101-107
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• 1997

In order to detect and suppress chatter in turning process, a stability control methodology was studied through manipulation of spindle speeds regarding to chatter frequencies, The chatter frequency was identified by monitoring and signal processing of sound pressure during turing on a lathe. The stability control methodology can select stable spindle speeds without knowing a prior knowledge of machine compliances and cutting dynamics. Reliability of the developed stability control methodology was verified through turing experiments on an engine lathe. Experimental results show that a microphone is an excellent sensor for chatter detection and control .

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.6
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• pp.486-491
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• 2000

The dicing process cuts a semiconductor wafer to lengthwise and crosswise direction by using a rotating circular diamond blade. But inferior goods are made under the influence of several parameters in dicing such as blade, wafer, cutting water and cutting conditions. This paper describes a monitoring algorithm using neural network in order to find out an instant of vibration signal change when bad dicing appears. The algorithm is composed of two steps: feature extraction and decision. In the feature extraction, five features processed from vibration signal which is acquired by accelerometer attached on blade head are proposed. In the decision, back-propagation neural network is adopted to classify the dicing process into normal and abnormal dicing, and normal and damaged blade. Experiments have been performed for GaAs semiconductor wafer in the case of normal/abnormal dicing and normal/damaged blade. Based upon observation of the experimental results, the proposed scheme shown has a good accuracy of classification performance by which the inferior goods decreased from 35.2% to 6.5%.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.3
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• pp.13-19
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• 2003

The high speed machining technology has been improved remarkably in die/mold industry with the growth of parts and materials industries. Though the spindle speed of machine tool increases, the condition monitoring techniques of the machine tool, tool and workpiece in high speed machining ate incomplete. In tins study, efficient sensing technology in high speed machining is suggested by observing the characteristics of cutting force, gap sensor and accelerometer signal also, machinability of high-speed machining is experimentally evaluated sensing technique to monitor the machine tool and machining conditions was performed.