• Tribology and Lubricants
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• 제9권1호
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• pp.38-44
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• 1993

The present study was undertaken to investigate the dry wear characteristics of die steel STD 11 for cold molding. The wear test was experimentally carried out under different conditions using a wear device, which was made in laboratory, and in which annular surfaces of wear testing specimens wear rubbed in dry sliding condition with varying the sliding speed, contact pressure, and sliding distance. The wear loss by variation of sliding speed was much in 0.3 m/sec and less in higher speed range above its sliding speed according to formation of the boundary lubrication film. The critical sliding speed with maximum value of the specific wear rate switched over to lower speed side according. as contact pressure increased. The critical sliding distance was increased with decrease in oxidation reaction velocity. The depth below subsurface showing maximum hardness (Hv) came out at the position, $60 \mu m$, of the maximum shear stress due to strain hardening.

• Tribology and Lubricants
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• 제11권3호
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• pp.24-30
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• 1995

This paper was undertaken to recognize the pattern of the wear debris by neural network as a link for the development of diagnosis system for movable condition of the lubricated machine surface. The wear test was carried out under different experimental conditions using the wear test device was made in laboratory and wear testing specimen of the pin-on-disk type were rubbed in paraffine series base oil, by varying applied load, sliding distance and mating material. The neural network has been used to pattern recognition of four parameter (diameter, elongation, complex and contrast) of the wear debris and learned the friction condition of five values (material 3, applied load 1, sliding distance 1). The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by the neural network. The characteristic parameter of the large wear debris over a few micron size enlarged recognition ability.

• Tribology and Lubricants
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• 제12권3호
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• pp.72-78
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• 1996

This paper was undertaken to analyze the morphology of wear debris generated from lubricated moving machine surfaces by image processing. The lubricati, ng wear test was performed under different experimental conditions using the wear test device made in our laboratory and wear test specimen of the pin on disk type wear rubbed in paraffme 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 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 to overcome many of the difficulties with current methods and to facilitate wider use of wear particle analysis in machine condition monitoring.

• 한국정밀공학회지
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• 제20권10호
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• pp.56-64
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• 2003

The effect of tool geometry on the tool wear in turning the austenitic stainless steel, STS 304 was investigated. The wear of TiN-TiCN-TiC-TiAlN coated tungsten carbide tool was the smallest, showing 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 the larger side cutting edge angle showed the smallest tool wear in all 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.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 1998년도 제28회 추계학술대회
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• pp.115-123
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• 1998

The morphological analysis of wear particle is a very effective means for machine condition monitoring and fault diagnosis. In order to describe morphology of various wear particle, the wear test was carried out under different experimental conditions. And fractal descriptors was applied to boundary and surface of wear particle with image processing system. These descriptors to analyze shape and surface wear particle are shape fractal dimension and surface fractal dimension. The shape fractal dimension can be derived from the boundary profile and surface fractal dimension can be determined by sum of intensity difference of surface pixel. The morphology of wear particles can be effectively obtained by two fractal dimensions.

• Tribology and Lubricants
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• 제10권3호
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• pp.39-46
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• 1994

In order to investigate the effect of the ceramic reinforcements on the wear properties of aluminum bronze composites, Cu-8wt%Al aluminum bronze alloys reinforced with the Saffil alumina short fiber were produced by the powder metallurgical method and tested by a pin-ondisc wear testing machine. The wear surfaces of the pin specimens and discs, wear debris, and the cross sections of the wear specimens were observed by SEM. The wear mechanism according to various wear conditions and the change of microstructure in the composites were also discussed. In the results, the reinforcement of the composites with alumina short fiber was very effective at the higher applied load over 10N. The material transportation to the counter disc was observed in the alloy specimens without reinforcements. However, the composites reinforced with ceramic particles and fibers showed the resistance against the material transportation.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.74-79
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• 1996

The wear of partially coated pinion cutters under several cutting conditions was studied. In the realm of this experiment, chipping was a dominant tool wear mechanism and flank wear was much larger than crater wear. Under the condition of relatively low rotary feed and low radial feed rate, the wear due to chipping was concentrated at the nose part of pinion cutter. Increasing of rotary feed and radial feed rate alleviated the concentration of chipping at nose and prolonged tool life.

• Tribology and Lubricants
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• 제18권2호
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• pp.147-152
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• 2002

The morphological analysis of wear particle is a very effective means fur machine condition monitoring and fault diagnosis. In order to describe morphology of various wear particle, the wear test was carried out under friction experimental conditions. And fractal descriptors was applied to boundary and surface of wear particle with image processing. These descriptors to analyze shape and surface of wear particle are shape fractal dimension and surface fractal dimension. The boundary fractal dimension can be derived from the boundary profile and surface fractal dimension can be determined by sum of intensity difference of surface pixel. The morphology of wear particles can be effectively obtained by two fractal parameter.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2001년도 제34회 추계학술대회 개최
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• pp.53-58
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• 2001

Fractal dimension is the method to measure the roughness and the irregularity of something that cannot be defined obviously by Euclidean dimension. And the analysis method of this dimension don't need perfect, accurate boundary and color like analysis lot diameter, perimeter, aspect or reflectivity of wear particles or surface. If we arranged the morphological characteristic of various wear particle by using the characteristic of fractal dimension, it might be very efficient to the diagnosis of driving condition. In order to describe morphology of various wear particle, the wear test was carried out under friction experimental conditions. And fractal descriptors was applied to boundary and surface of wear particle with image processing system. These descriptors to analyze shape and surface wear particle are boundary fractal dimension and surface fractal dimension.

• 한국공작기계학회논문집
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• 제12권5호
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• pp.53-58
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• 2003

Wear debris can be collected from the lubricants of operating machinery and its morphology is directly related to the fiction condition of the interacting materials from which the wear particles originated in lubricated machinery. But in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefore, if the shape characteristics of wear debris is identified by computer image analysis and the neural network, The four parameter (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction. It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We resented how the neural network recognize wear debris on driving condition.