• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.130-134
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• 1996

The characteristics of AE(Acoustic Emission) signal is related to cutting conditio, tool material, and tool geometry in metal cutting. The relation between AE signal and tool life was investigated experimentally. Experiment is carried out by gear shaping and SCM 420 workpiece. AE RMS voltage were increased according totool wear. It is suggested that maximum value of AE RMS voltage is an effective parameter to monitor tool life.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.189-198
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• 1998

Recently, monitoring of tool life is a matter of common interesting because tool life affects precision, productivity and cost in machining process. Especially flank wear has a direct effect on cutting mechanism, so the various pattern of cutting force is obtained experimentally according to variation of wear condition. By investigating cutting force signal, AR(Autoregressive) modeling and correlation dimension analysis is conducted in turning operation. In this modeling and analysis, we extract features through 6th AR model, correlation integral and normalized correlation integral. After the back-propagation model of the neural network is utilized to monitor tool life according to flank wear. As a result. a very reliable classification of tool life was obtained.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.956-959
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• 2004

Recently the monitoring system of tool setting in high speed precision machining center is required for manufacturing products that have highly complex and small shape, high precision and high function. It is very important to reduce time to setup tool in order to improve the machining precision and the productivity and to protect the breakage of cutting tool as the shape of product is smaller and more complex. Generally, the combination of errors that geometrical clamping error of fixing tool at the spindle of machining tool and the asynchronized error of driving mechanism causes that the run-out of tool reaches to 3$^{\sim}$20 times of the thickness of cutting chip. And also the run-out is occurred by the misalignment between axis of tool shank and axis of spindle and spindle bearing in high speed rotation. Generally, high speed machining is considered when the rotating speed is more than 8,000 rpm. At that time, the life time of tool is reduced to about 50% and the roughness of machining surface is worse as the run-out is increased to 10 micron. The life time of tool could be increased by making monitoring of tool-setup easy, quick and precise in high speed machining tool. This means the consumption of tool is much more reduced. And also it reduces the manufacturing cost and increases the productivity by reducing the tool-setup time of operator. In this study, in order to establish the concept of tool-setup monitoring the measuring method of the geometrical error of tool system is studied when the spindle is stopped. And also the measuring method of run-out, dynamic error of tool system, is studied when the spindle is rotated in 8,000${\sim}$60,000 rpm. The dynamic phenomena of tool-setup are analyzed by implementing the monitoring system of rotating tool system and the non-contact measuring system of micro displacement in high speed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.69-73
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• 1996

Tool wear monitoring is very important aspect in metal cutting because tool wear effects quarity and precision of workpiece, tool life etc. In this study we detected force signal through tool dynamometer in turning and using it we conducted 6th AR modeling and fractal analysis. Finally the back-propagation model of the neural network is utilized to monitor tool wear and features are extracted through AR model and fractal analysis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.311-315
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• 2000

In thls paper, a remote machine monitoring system for a vimal machine is proposed. The monltonng system is one of the core functmns of a vimd machne that provides a modeling and simulation environment for machining processes and management of the machine life cycle. The proposed system contains the modules for investigating tool wear using neural network and web-based real time process monitoring. An example implementation for tool wear and machining status monitoring is illustrated

• Journal of the Korean Society for Precision Engineering
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• v.2 no.1
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• pp.62-71
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• 1985

This is a study on the monitoring technique in tool failure and tool life by AE method. The relation between amplitude level of AE signal and flank wear width was studied by experiments. The relation between amplitude level of AE signal and tool life was also studied. As the result, it was observed that amplitude level of AE signal was only affected by cutting velocity. Amplitude level of AE signal was directly proportional to flank wear width and the increasing rate was related to cutting velocity. So, the relation between amplitude level of AE signal and tool life was represented as follow: $CT^n$ = $AE_{rms}$ where, n=0.35 C=9.5*$10^{-2}$

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.94-100
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• 2000

This paper presents a methodology for In-Process monitoring of tool wear by using ultrasonic sensor in turning operation. An integrated single ultrasonic transducer operation at a frequency of 10MHz is placed in contact with the insert tip. The change in amount of the reflected energy from the nose and flank of the tool can be related to the level of tool wear and the mechanical integrity of the tool. As the results, the tool wear monitoring system based on the ultrasonic pulse-echo method was proposed, it is useful to determine a tool life and tool change time.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.3
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1066-1069
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• 2004

Recently the monitoring system of tool setup in high speed precision machining tool is required for manufacturing products that have highly complex and small shape, high precision and high function. It is very important to reduce time to setup tool in order to improve the machining precision and productivity and to protect the breakage of cutting tool as the shape of product is smaller and more complex. Generally, the combination of errors that geometrical clamping error of fixing tool at the spindle of machining center and the asynchronized error of driving mechanism causes that the run-out of tool reaches to 3∼20 times of the thickness of cutting chip. And also the run-out is occurred by the misalignment between axis of tool shank and axis of spindle and spindle bearing in high speed rotation. Generally, high speed machining is considered when the rotating speed is more than 8,000 rpm. At that time, the life time of tool is reduced to about 50％ and the roughness of machining surface is worse as the run-out is increased to 10 micron. The life time of tool could be increased by making monitoring of tool-setting easy, quick and precise in high speed machining center. This means the consumption of tool is much more reduced. And also it reduces the manufacturing cost and increases the productivity by reducing the tool-setup time of operator. In this study, in order to establish the concept of tool-setting monitoring the measuring method of the geometrical error of tool system is studied when the spindle is stopped. And also the measuring method of run-out, dynamic error of tool system, is studied when the spindle is rotated in 8,000 ∼ 60,000 rpm. The dynamic phenomena of tool-setup is analyzed by implementing the monitoring system of rotating tool system and the noncontact measuring system of micro displacement in high speed.