• Journal of Korea Foundry Society
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• v.3 no.2
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• pp.92-99
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• 1983

This study has been carried out to investigate into the difference of rolling life and rolling wear characteristics for various gray cast iron under unlubricated dry rolling condition by amsler type wear test with 9.09% sliding.The results obtained from this study are summerized as follows: 1) It has been found that the amount of rolling wear id decreased when tensile strength and hardness are low, and then the rolling life up to generation of abnormal wear is conspicuously increased. 2) At the given condition the amount of rolling wear has been found to decrease as carbon equivalent of gray cast iron increases and resistance of crack propagation is an important factor on improvement of wear characteristics. 3) The amount of rolling wear is increased with increasing rolling revolution and wear of gray cast iron under dry rolling condition is characterized by three modes; initial wear, stationary wear and abnormal wear. 4) It has been found that the amount of rolling wear is increased with increasing maximum compressive stress and extremely increased when maximum compressive stress is over 59.1kg.f/mm.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.6
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• pp.127-132
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• 2000

Morphological analysis of wear particles is one of useful methods for machine condition monitoring because it is well reflected in machine driving state. This paper was undertaken to apply to the condition monitoring of hydraulic piston motor. 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 particles. The results showed that the four shape parameters of wear particles depend on a kind of the lubricating materials. It was capable of calculating presumed wear volume for three kinds of materials on driving time to foresee as damage condition of lubricating materials.

• Tribology and Lubricants
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• v.13 no.1
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• pp.21-27
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• 1997

The friction and wear behavior of undulated surfaces made of tin base babbit are examined experimentally at the low sliding speed with severe loading condition. Steel is used as counterface disk material under pin-on-disk type sliding condition. Undulated surfaces can improve the friction and wear properties under dry friction condition since undulated surfaces trap wear particles in their cavities and prohibit wear particles from agglomerating. However, under boundary lubrication condition, friction and wear properties of undulated surfaces are inferior to those of flat surfaces. It is shown that land width and the ratio of wear volume to cavity volume are the most important factors in friction behavior of undulated surfaces under dry friction condition, and there exists optimum land width minimizing friction and wear of undulated surfaces.

• Journal of the Korean Society for Heat Treatment
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• v.27 no.2
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• pp.65-71
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• 2014

SMF9060 material is a Fe-based powder sintered alloy that is used for several automobile components such as Synchronize Hub, oil pump and transmission. These components are required excellent wear resistance and durability. In this study, we have performed a dry wear test at the ambient air and Ar gas conditions in the room temperature, and a lubricant wear test at the room temperature and engine oil temperature of $100^{\circ}C$. The amount of wear volume and coefficient friction are measured by a Profilometer and a Ball on disk type wear tester. The wear volume in Ar gas condition was a little higher than that in the ambient air condition. However the wear volume in the lubricant wear condition was much lower than in the dry wear condition. XRD analysis of the debris in Ar gas condition showed that the oxide film was not formed.

• 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.

• Tribology and Lubricants
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• v.35 no.6
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• pp.389-395
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• 2019

In various industries, thin film coatings are used to improve friction and wear characteristics. Various types of tribotesters are used to evaluate the friction and wear characteristics of such thin film coatings. In this study, we fabricated a micro-tribotester and Tribo-scanning electron microscopy (SEM) to compare the friction and wear characteristics of copper (Cu) coatings under an atmospheric pressure and a vacuum condition, respectively. The reliability of the different types of tribotesters was evaluated by performing calibrations for the sensor to measure the friction forces and normal loads. Using the two different types of devices, the friction and wear tests are conducted at the same experimental conditions excluding environment conditions such as the atmospheric pressure and vacuum condition. The friction coefficient at the vacuum condition is lower than at the atmospheric pressure. This difference in friction characteristics is due to the fact that wear phenomena occur differently according to the atmospheric pressure and vacuum condition. At the atmospheric pressure, the abrasive wear is the main wear mechanism. At the vacuum condition, the adhesive wear is the main wear mechanism. The reason for the difference in the wear mechanism of the Cu coating at the atmospheric pressure and the vacuum condition is that the oxidation phenomenon, which does not appear at the vacuum condition, occurs at the atmospheric pressure; therefore, the characteristics of the Cu coating change accordingly.

• Tribology and Lubricants
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• v.14 no.3
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• pp.74-80
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• 1998

The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.136-142
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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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.5
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• pp.92-97
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• 1998

The morpholgies of the wear particles are directly indicative of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the machine driving system. The four parameters(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 parameters learned. The three kinds of the wear debris had a different patter characteristic and recognized the friction condition and materials very well by artificial neural network. We discussed how the network determines differencee in wear debris feature, and this approach can be applied to foreseeability and decisio for moving condition of the Machine driving system.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.05a
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• pp.193-199
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• 2002

Analyzing working conditions with shape characteristics of wear debris in a lubricated machine, it can be effect on diagnosis of hydraulic machining system. And it can be recognized that results are processed threshold images of wear debris. But, in order to predict and estimate a working condition of lubricated machine, it is need to analysis a shape characteristic of wear debris and to identify. Therefor, If shape characteristics of wear debris are identified by computer image analysis and the neural network, it is possible to find the cause and effect of wear condition. In this stud)r, wear debris in the lubricant oil are extracted by membrane filter $(0.45{\mu}m)$, and the quantitative value of shape characteristic of wear debris are calculated by the digital image processing. This morphological information are studied and identified by tile artificial neural network. The purpose of this study is to apply morphological characteristic of wear debris to prediction and estimation of working condition in hydraulic machining systems.