• Korean Chemical Engineering Research
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• v.50 no.5
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• pp.913-922
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• 2012

Grasp of characteristics within liquid-liquid agitated vessel are very important to environment and chemical industry. Mass transfer volumetric coefficient and the Sauter mean diameter of near the droplet were measured by varying the impeller position and liquid height using the alkaline hydrolysis reaction of esters. As a result, following their good correlation was obtained. $$d_{32}=0.270\(\frac{{\sigma}^{0.6}}{{\rho}^{0.2}P^{0.4}_{Vi}}\)k_La=0.49\(\frac{6{\phi}D_A}{d^2_{32}}\)\(\frac{P_Vd^4_{32}}{{\rho}v^3}\)^{0.193}Sc^{1/3}$$.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1997.04a
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• pp.224-231
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• 1997

Microscopic examination for the morphological estimation of wear debris on the oil-lubrcated moving system is an accepted method for machine condition and fault diagnosis. However wear particle anaysis has not been widely accepted industry because it is dependent on expert interpretation of particle morphology and relies on subjective assessment criteria. This paper was undertaken to estimate the morphology of wear debris on the oil-lubricated movig system by computer image analysis. The wear test was performed under different sliding conditions using a wear test device made in our laboratory and wear testing specimen of the pin-on-disk-type was rubbed in pararline series base oil. In order to describe characteristics of debris of various shape and size, four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.85-89
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• 1996

This paper was undertaken to analyze the morphology of wear debris for operating condition evaluatio of lubricated machine surfaces. The lubricating wear test was carried out under different experimental conditions using tile wear test device was made in our laboritory and wear testing spcimen of the pin on disk type was rubbed in paraffine series base oil, by varying specimen, applied load, sliding distance. The four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) to describe morphology of wear debris have been developed and are outlined in tile paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology in machine condition monitoring

• 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.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1996.04b
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• pp.60-67
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• 1996

This paper was undertaken to analyze the morphology of wear debris on operating condition of lubricated machine surfaces. The lubricating wear test was carried out under different experimental conditions using the wear test device was made in our laboritory and wear testing spcimen of the pin on disk type was rubbed in paraffine series base oil, by varying specimen, applied load, sliding distance. The four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) to describe morphology of wear debris 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 for machine condition monitoring.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.143-149
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• 1997

This paper was undertaken to do image analysis of wear debris on operating condition of the lubricated moving surfaces. This lubricating wear test was performed under different experimental conditions using the wear test device was made in our laboratory and wear testing specimen of the pin on dist type was rubbed in paraffine series base oil, by materials, varying applied load, sliding distance. The four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) to describe wear debris 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 for machine condition monitoring, this to overcome many of the difficulties with current methods and facilitating wider use of wear particle analysis in machine condition monitouing.

• Tribology and Lubricants
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• v.12 no.3
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.835-841
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• 1997

Microscopic examination of the morphology of wear debris is an accepted method for machine condition and fault diagnosis. However wear particle analysis has not been widely accepted in industry because it is dependent on expert interpretation of particle morphology and subjective assessment criteria. This paper was undertaken to analyze the morphology of wear debris for machine condition diagnosis of the lubricated moving surfaces by image processing and analysis. The lubricating wear test was performed under different sliding conditions using a wear test device made in our laboratory and wear testing specimen of the pin-on-disk-type was rubbed in paraffine series base oil. In order to describe characteristics of debris of various shape and size, four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) 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 in current methods and to facilitate wider use of wear particle analysis in machine condition monitoring.

• KSTLE International Journal
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• v.2 no.1
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• pp.65-70
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• 2001

The wear debris is fall off the moving surfaces in oil-lubricated systems and its morphology is directly related to the damage and failure to the interacting surfaces. The morphology of the wear particles are therefore directly indicative of wear processes occurring in tribological system. The computer image processing and artificial neural network was applied to shape study and identify wear debris generated from the lubricated moving system. In order to describe the characteristics of various wear particles, four representative parameter (50％ volumetric diameter, aspect, roundness and reflectivity) from computer image analysis for groups of randomly sampled wear particles, 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 parameters 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. We discuss how these approach can be applied to condition diagnosis of the oil-lubricated tribological system.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.5
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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.