• Tribology and Lubricants
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• v.17 no.1
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• pp.33-39
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• 2001

A method of calculating wear volume is developed using the signal processing technique. The lowpass filter with Fourier transform and the “windowing” are implemented in the method. User-defining feature is also included in determining the cutoff frequency of the low-pass filter and the baseline for the volume integration. Commercial software, MatLab, is used for the programming. Since the method uses the original wear data without simplifying the wear shape, it can give a further accurate result than the previously utilized methods, which often adopted the simplification. It becomes further powerful if the contacting body has a general shape rather than that gives well-formed surface traction (e.g., the Hertzian). The validation of applying the average surface roughness, Ra, to the “windowing” and the baseline for volume integration is discussed.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1998.10a
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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.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.646-649
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• 2002

It can be effect on diagnosis of hydraulic machining system to analyze working conditions with shape characteristics of wear debris in a lubricated machine. But, in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefor, if shape characteristics of wear debris is identified by computer image analysis and the neural network, it is possible to find the cause and effect of moving condition. In this study, wear debris in the lubricant oil are extracted by membrane filter, and the quantitative value of shape characteristics of wear debris we calculated by the digital image processing. This morphological informations are studied and identified by the artificial neural network. The purpose of this study is In apply morphological characteristics of wear debris to prediction and estimation of working condition in hydraulic driving systems.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.11a
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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.

• Transactions of Materials Processing
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• v.9 no.1
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• pp.43-51
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• 2000

The die wear is one of the main factors affecting product accuracy and die life in hot forging process. It is desired to analyze die wear by developing wear prediction method combined with FE-simulatin and experiment. Lots of researches have been done into the wear analysis of cold forging die, and the results of those researches were successful, but there have been little applications to hot forging die giving successful results. That is because hot forging process has many factors influencing die wear, and there was not accurate in-process data. In this research, change of die surface hardness by thermal softening during the lifetime was obtained by experiment, and hardness distribution of cross section was measured. This wear analysis was applied to hot forging die, and gave comparatively good results compared with actual wear profile.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.12
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• pp.2970-2981
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• 1993

In hot closed-die forging the load increases rapidly near the final stage. Preforming operation is important to both the sound final forging and die-service life. In this study, the material flows during preforming and final forging are investigated. The physical modeling with Plasticine as a model material showed clear flow patterns. The forging process were numerically simulated by the finite element method with the isothermal and the non-isothermal models. The flow patten of the isothermal simulation showed good agreements with the experiments. Temperature changes and pressure distributions on the die surfaces during one cycle of the forging process were obtained from the non-isothermal simulation. High pressure and temperature were developed at certain areas of the die surfaces. It was concluded that those areas usually coincide with each other and should be distributed by the preforming operations to enhance the die life.

• 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.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.6
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• pp.1290-1295
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• 1988

Theoretical considerations in the development of new cutting tool materials for high speed machining is presented. The progressive wear of cutting tools is assumed to consist of the abrasive and solution components as major modes. Theoretical calculations of relative wear rates between various tool materials based on the two modes are possible using their hardness and solubility data. Assuming cementite as the major hard particles in machining steels, relative wear rates of possible tool materials were calculated. The results indicate that $Al_{2}$O$_{3}$ in oxides, HfN in nitrides and HfC in carbides are the optimal tool materials from the view point of mechanical and thermochemical wear resistance. And several methods for improving the fracture toughness of the above tool materials are suggested.

• Journal of the Korean Society of Safety
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• v.18 no.4
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• pp.16-22
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• 2003

Generally the rupture of steam generator tubes proceeds from significant plastic deformation before failure. In this study, the burst pressures of damaged steam generator tubes were calculated from the plastic instability analysis with the finite element method. Two wear types, flat and circumferential types were considered. An equation for the burst pressure was proposed by using the strength reduction factor and the Svensson equation. The analysis results were compared with the experiment data from published references and they showed a good agreement with the experiment data.