• Title/Summary/Keyword: wear debris

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A Study on Recognition of Operating Condition for Hydraulic Driving Members

  • Park, Heung-Sik;Kim, Young-Hee;Kim, Dong-Ho;Cho, Yon-Sang;Park, Jae-Sang
    • International Journal of Precision Engineering and Manufacturing
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    • v.4 no.6
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    • pp.44-49
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    • 2003
  • The morphological analysis of wear debris can provide early a failure diagnosis in lubricated moving system. It can be effective to analyze operating conditions of oil-lubricated tribological system with shape characteristics of wear debris in a lubricant. But, in order to predict and recognize an operating condition of lubricated machine, it is needed to analyze and to identify shape characteristics of wear debris. Therefore, If the morphological characteristics of wear debris are recognized by computer image analysis using the neural network algorithm, it is possible to recognize operating condition of hydraulic driving members. In this study, wear debris in the lubricating oil are extracted by membrane filter (0.45$\mu\textrm{m}$), and the quantitative values of shape parameters of wear debris are calculated by the digital image processing. This shape parameters are studied and identified by the artificial neural network algorithm. The result of study could be applied to prediction and to recognition of the operating condition of hydraulic driving members in lubricated machine systems.

Wear Debris Identification of the Lubricated Machine Surface with Neural Network Model (신경회로망 모델을 이용한 기계윤활면의 마멸분 형태식별)

  • 박홍식;서영백;조연상
    • 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.

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Analysis of Wear Debris on the Lubricated Machine Surface by the Neural Network (Neural Network에 의한 기계윤활면의 마멸분 해석)

  • 박흥식
    • Tribology and Lubricants
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    • v.11 no.3
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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.

Decision of Lubricated Friction Conditions for Materials of Automobile Transmission Gear Using Neural Network

  • Cho Yon-Sang;Park Heung-Sik
    • Journal of Mechanical Science and Technology
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    • v.20 no.5
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    • pp.583-590
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    • 2006
  • It is hard to inspect the state of lubrication of an automobile transmission visually. Thus, it is necessary to develop a new inspection method. Wear debris can be collected from the lubricants of an operating transmission of an automobile, and its morphology will be directly related to the friction condition of the interacting materials from which the wear debris originated in the lubricated transmission. In this study, wear debris in lubricating oil are extracted by membrane filter $(0.45{\mu}m)$, and the quantitative values of shape parameters of wear debris are calculated by digital image processing. These shape parameters are studied and identified by an artificial neural network algorithm. The results of the study may be applicable to the prediction and diagnosis of the operating condition of transmission gear.

Decision of Operating Condition in the Lubricated Moving System by Neural Network (신경회로망에 의한 윤활 구동계의 작동조건 판정)

  • 조연상;문병주;박흥식;전태옥
    • Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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    • pp.135-144
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    • 1997
  • This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surfaces from which the particles originated. The morphologies of the wear particles are therefore directly indica- rive of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the lubricated moving system. The four 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 characteristic and recognized the friction condition and materials very well by neural network. We dicuss how the network determines difference in wear debris feature, and this approach can be applied to condition diagnosis of the lubricated moving system.

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Shape Study of Wear Debris in Oil-Lubricated System with Neural Network

  • Park, Heung-Sik;Seo, Young-Baek;Cho, Yon-Sang
    • 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.

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Presumption of Slipper-pad Fault Condition for Hydraulic Rotary Actuator (마멸입자 해석을 통한 유압로터용 Slipper - Pad의 손상상태 추정)

  • 전성재;조연상;서영백;박흥식
    • Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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    • pp.62-67
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    • 2000
  • This paper was undertaken to do morphological analysis of wear debris for slipper-Pad of hydraulic 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 for morphological analysis of wear debris. The results 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 hydraulic rotary acuator on driving time.

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Wear Debris Analysis using the Color Pattern Recognition (칼라 패턴인식을 이용한 마모입자 분석)

  • ;A.Y.Grigoriev
    • Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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    • pp.54-61
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    • 2000
  • A method and results of classification of 4 types metallic wear debris were presented by using their color features. The color image of wear debris was used (or the initial data, and the color properties of the debris were specified by HSI color model. Particle was characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used for the definition of classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

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Reducing the friction and the wear of carbon fiber composites with micro-grooves (미소채널 구조를 이용한 탄소 섬유 복합재료 면의 마찰 및 마모 감소)

  • Lee H.G.;Lee D.G.
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.855-859
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    • 2005
  • Carbon fiber polymeric composites have been widely used in bearing materials under high pressure without oil-lubrication due to their self-lubricating characteristics. However, the severe wear of carbon composite surface occurs due to the generation of wear debris when the pressure applied on the composite surface is higher than the critical value of composite surface. In this work, in order to remove wear debris continuously during sliding operation, composite specimens with many micro-grooves on their sliding surfaces were devised. To investigate the effect of wear debris on the tribological behavior of carbon/epoxy composites, dry sliding tests were performed with respect to applied pressure using the composite specimens with and without micro-grooves. From the measurement of friction coefficients and wear rates, a model for the effect of wear debris on the friction and wear of composites was proposed.

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Quantitative Analysis of Wear Debris for Surface Modification Layer by Ferrography (Ferrography에 의한 표면개질층의 마모분 정량분석)

  • 오성모;이봉구
    • Tribology and Lubricants
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    • v.15 no.3
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    • pp.265-271
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    • 1999
  • Wherever there are rotating equipment and contact between surface, there is wear and the generation of wear particles. The particles contained in the lubricating oil carry detailed and important information about the condition monitoring of the machine. This information may be deduced from particle shape, composition, size distribution, and concentration. Therefore, This paper was undertaken to Ferrography system of wear debris generated from lubricated moving machine surface. The lubricating wear test was performed under different experimental conditions using the Falex wear test of Pin and V-Block type by Ti(C, N) coated. It was shown from the test results that wear particle concentration (WPC) and wear severity Index( $I_{S}$), size distribution in normal and abnormal wear have come out all the higher value by increases sliding friction time. Wear shape is observed on the Ferrogram it was discovered a thin leaf wear debris as well as ball and plate type wear particles. This kind of large wear shape have an important effect not only metals damage, but also seizure phenomenon.