• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.95-100
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• 1995

Automatic monitoring of cutting process is one of the most important technology for increasing the stability and the reliability of unmanned manufacturing system. In this study, basic methods which use the acoustic emission (AE) signals and sutting forces proposed to monitor tool wear (flank wear) quantitatively. Fist, in order to detect flank wear, it was investigated influence of cutting conditions, that is, cutting velocity, feed and depth of cut, on AE signals (AErems) and cutting forces. Furthermore,the relationship flank wear between AErems and cutting forces were discussed.

• Tribology and Lubricants
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• v.13 no.4
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• pp.71-78
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• 1997

Wear is affected by numerous factors-contact load, sliding velocity and distance, friction coefficient, material properties and environmental conditions. Among these wear factors, surface hardness is one of very important factors to determine wear. But surface hardness is varied by work hardening during repeated sliding contact. In this reason wear rate is increased or decreased with varying surface hardness, and transition of wear mechanism is happened. In this study, the surface hardening by accumulating residual stress was analyzed by considering the repeated sliding Hertzian contact model. The results showed that surface hardness was increased with increasing contact load, friction coefficient and contact number. And the depth of hardening layer, plastic layer and elastic layer depended upon contact load and number, but they didn't depend upon friction coefficient. The predicted surface hardness was about 1.5-1.8 times as hard as the material.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.563-567
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• 1996

This paper was undertaken to analyze the morphology of wear debris generating from moving lubricated machine surfaces by image processing. The lubricating wear test was carried out under different experimentaal conditions using the wear test device was made in our laboritory and wear testing specimen of the pin on disk type wear rubbed in paraffine series base oil, byvarying applied load, sliding distance. The four parameters(50% volumetric diameter, aspect, roundness and reflectivity) to describe the morphology have been developed and are outlined in the paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology in machine condition monitoring, thus overcoming many of the difficulties with current methods and facilitating wider use of wear particle analysis in machine condition monitoring.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.786-793
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• 2000

Nylon, Polyacetal and PTFE were studied to gain a better understanding of their tribological behavior. Wear tests were conducted with reciprocating motion under dry sliding conditions. Friction coefficient and specific wear rate were measured as a function of sliding distance. The worn surfaces were examined with a Scanning Electron Microscope(SEM). Polyacetal showed lowest specific wear rates and PTFE exhibited lowest friction coefficient. The dominant wear mechanism found were adhesion and abrasion.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.581-584
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• 2003

The austenitic STS 304 stainless steel was turned to clarify the effects of tool geometry on the tool wear. The wear of TiN-TiCN-TiC-TiAlN coated tungsten carbide tool was the smallest, exhibiting larger wear in the order of Si-Al-O-N ceramic, TiN coated tungsten carbide, TiN-TiCN-TiN coated tungsten carbide, TiC-TiN cermet and M20 tungsten carbide tools at the same cutting conditions. The S-type tool of M20 with large approach angle showed the longest tool life of all tools used in this tests due to preventing the groove wear of the side cutting edge. The wear of the S-type tool with the rake angle of 15$^{\circ}$became smaller than with that of -5$^{\circ}$, but the tool with the nose radius of 0.8mm did not perform much better with increasing the rake angle.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.10a
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• pp.164-169
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• 1998

This paper deals with condition monitoring for tool wear during tuning operation. To develop economic sensing and identification methods for turning processes, sound pressure measurement and digital signal processing technique are proposed. To identify noise sources of tool wear and reject background noise, noise rejection methodology is proposed. features to represent condition of tool wear are obtained through analysis using adaptive filter and FFT in time and frequency domain. By using fuzzy pattern recognition, we extract features, which are sensitive to condition of tool wear, from several features and make a decision on tool wear. The validity of the proposed system is condirmed through the large number of cutting tests in two cutting conditions.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1996.05a
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• pp.76-82
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• 1996

This paper was undertaken to analyze the morphology of wear debris generating from moving lubricated machine surfaces by image processing. The lubricating wear test was carried out under different experimental conditions using the wear test device was made in our laboritory and wear testing specimen of the pin on disk type wear rubbed in paraffine series base oil, by varying applied load, sliding distance. The four parameters(50% volumetric diameter, aspect, roundness and reflectivity) to describe the morphology have been developed and are outlined in the paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology in machine condition monitoring, thus overcoming many of the difficulties with current methods and facilitating wider use of wear particle analysis in machine condition monitoring.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.285-290
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• 2000

This paper was undertaken to do morphological analysis of wear debris for slipper-pad of hydrauric rotary acuator. The lubricating wear test was performed under different experimental conditions using the wear test device and wear specimens of the pin on disk type was rubbed in paraffinic base oil by three kinds of lubricating materials. varying applied load, sliding distance. The four shape parameters(50% volumetric diameter, aspect, roundness and reflectivity) are used showed that the four shape parameters of wear debris depend on a kind of the lubricating condition. It was capable of presuming wear volume for slipper-pad of hydrauric rotary acuator on driving time.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1999.11a
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• pp.35-41
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• 1999

Friction and wear test was performed for nylon, acetal resin, and PTFE(polytetrafluoroethylene) under reciprocating dry sliding conditions against a steel counterpart. Friction coefficient and specific wear rate were analysed as a function of sliding distance and applied load. The worn surfaces of plate and disc specimens were observed using SEM(Scanning Electron Microscopy). The experimental results show that acetal resin exhibited lowest wear rates whereas PTFE possessed lowest friction coefficient. The prominent wear mechanisms found were adhesion and abrasion.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.133-140
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• 1998

The neural network was applied to identify wear debris generated from the lubricated machine surface. The wear test was carried out under different experimental conditions. In order to describe characteristics of debris of various shapes and sizes, the four shape parameter(50％ volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the friction condition and materials very well by neural network.