• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.455-456
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• 2002

Wear is one of the main factors causing breakdown and fault of machine, so ferrography technique analyzing wear particles can be an effective way for condition monitoring and fault diagnosis. On the base of the forward multilayer neural network, a nodes self-deleting neural network model is provided in this paper. This network can itself deletes the nodes to optimize its construction. On the basis of the nodes self-deleting neural network, an intelligent ferrography diagnosis expert system (IFDES) for wear particles recognition and wear diagnosis is described. This intelligent expert system can automatically slim lip knowledge by learning from samples and realize basically the entirely automatic processing from wear particles recognition to wear diagnosis.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.1
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• pp.102-110
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• 1998

The analysis of lateral hydrodynamic forces in the leakage path between a shrouded pump impeller through wear-ring seal and its housing is presented. Governing equations are derived based on Bulk-flow and Hirs' turbulent lubrication model. By using a perturbation analysis and a numerical integration method, governing equations are solved to yield leakage and rotordynamic coefficients of force developed by the impeller shroud and wear-ring seal. The variation of rotordynamic coefficients of pump impeller shroud and wear-ring seal is analyzed as parameters of rotor speed, pressure difference, shroud clearance, wear-ring seal clearance, and circumferential velocity at the entrance of impeller shroud for a typical multi-stage centrifugal pump.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.182-190
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• 1999

The objective of this study is to develop an analysis scheme in order to predict regrinding due to tool wear in shearing process. The analysis of material now and fracture in shearing process should precede the prediction of tool wear. Thus the developed FE-program to analyze shearing process is used. In order to predict tool wear, the wear model is reformulated as an incremental form and then the wear depth of tool is calculated at each deformation path. Because the regrinding of shearing tool is determined on the basis of allowable size of burr, the analysis of shearing process is iteratively performed using the worn profile of tool. To show the effectiveness of the scheme the simulation result is compared with experimental one.

• Tribology and Lubricants
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• v.13 no.4
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• pp.71-78
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• 1997

Wear is affected by numerous factors-contact load, sliding velocity and distance, friction coefficient, material properties and environmental conditions. Among these wear factors, surface hardness is one of very important factors to determine wear. But surface hardness is varied by work hardening during repeated sliding contact. In this reason wear rate is increased or decreased with varying surface hardness, and transition of wear mechanism is happened. In this study, the surface hardening by accumulating residual stress was analyzed by considering the repeated sliding Hertzian contact model. The results showed that surface hardness was increased with increasing contact load, friction coefficient and contact number. And the depth of hardening layer, plastic layer and elastic layer depended upon contact load and number, but they didn't depend upon friction coefficient. The predicted surface hardness was about 1.5-1.8 times as hard as the material.

• 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 Reliability Society Conference
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• 2005.06a
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• pp.121-129
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• 2005

In this paper, we consider a periodic preventive maintenance(PM) policy in which each PM reduces the hazard rate of amount proportional to the failure intensity, which increases since the last PM and slows down the wear-out speed to that of new one. And the proportion of reduction in hazard rate decreases with the number of PMs. Our model is similar to $ARI_1$ proposed by Doyen and Gaudoin(2004) in the sense of reduction of hazard rate. Our model has totally different wear-out pattern of hazard rate after PM's, however, and the proportion of reduction depends on the number of PM's. Assuming that the system undergoes only minimal repairs at failures between PM's, the expected cost rate per unit time is obtained. The optimal number N of PM and the optimal period x, which minimize the expected cost rate per unit time are discussed. Explicit solutions for the optimal periodic PM are given for the Weibull distribution case.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.1
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• pp.74-80
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• 2016

Due to the increased use of power caused by industrial development, the importance of improving wear and friction in the contact region has emerged. Except for some parts, such as brakes or clutches and friction, seals and precision mechanical parts (e.g., pistons, bearings, valves, and cams) are important engine components that require low friction characteristics. In this study, the experimental method used to determine the friction characteristics was based on the type of rpm with the pin-on-disc test device, the element analysis program ANSYS was used to analyze the surfaces of the two metals rubbing together, and physical formation FEM models were used to study the properties and wear. The friction coefficient of variation was unsafe, but at the start of wear, it converged to a stable friction coefficient that increased after a certain slip away.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.165-172
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• 2017

Machines are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. Under the process mean shift, production cost, failure cost and quality loss function cost are increasing continuously. Therefore a periodic preventive resetting the process is necessary. We suppose that the wear level is observable. In this case, process mean shift problem has similar characteristics to the maintenance policy model. In the previous studies, process mean shift problem has been studied in several fields such as 'Tool wear limit', 'Canning Process' and 'Quality Loss Function' separately or partially integrated form. This paper proposes an integrated cost model which involves production cost by the material, failure cost by the nonconforming items, quality loss function cost by the deviation between the quality characteristics from the target value and resetting the process cost. We expand this process mean shift problem a little more by dealing the process variance as a function, not a constant value. We suggested a multiplier function model to the process variance according to the analysis result with practical data. We adopted two-side specification to our model. The initial process mean is generally set somewhat above the lower specification. The objective function is total integrated costs per unit wear and independent variables are wear limit and initial setting process mean. The optimum is derived from numerical analysis because the integral form of the objective function is not possible. A numerical example is presented.

• The Journal of Advanced Prosthodontics
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• v.12 no.1
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• pp.1-8
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• 2020

PURPOSE. To investigate the influence of crown material (lithium-disilicate, 3Y-TZP zirconia) and abutment type (rigid implant, resin tooth with artificial periodontium) on wear performance of their antagonist teeth and adjacent teeth. MATERIALS AND METHODS. A mandibular left first molar (#36) with adjacent human teeth (mandibular left second premolar: #35, mandibular left second molar: #37) and antagonistic human teeth (maxillary left second premolar: #25, maxillary left first molar: #26, maxillary left second molar: #27) was prepared simulating a section of the jaw. Samples were made with extracted human molars (Reference), crowned implants (Implant), or crowned resin tooth analogues (Tooth). Crowns (tooth #36; n = 16/material) were milled from lithium-disilicate (Li, IPS e.max CAD) or 3Y-TZP zirconia (Zr, IPS e.max ZirCAD, both Ivoclar Vivadent). Thermal cycling and mechanical loading (TCML) in the chewing simulator were applied simulating 15 years of clinical service. Wear traces were analyzed (frequency [n], depth [㎛]) and evaluated using scanning electron pictures. Wear results were compared by one-way-ANOVA and post-hoc-Bonferroni (α = 0.05). RESULTS. After TCML, no visible wear traces were found on Zr. Li showed more wear traces (n = 30-31) than the reference (n = 21). Antagonistic teeth #26 showed more wear traces in contact to both ceramics (n = 27-29) than to the reference (n = 21). Strong wear traces (> 350 ㎛) on antagonists and their adjacent teeth were found only in crowned groups. Abutment type influenced number and depth of wear facets on the antagonistic and adjacent teeth. CONCLUSION. The clinically relevant model with human antagonistic and adjacent teeth allowed for a limited comparison of the wear situation. The total number of wear traces and strong wear on crowns, antagonistic and adjacent teeth were influenced by crown material.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.180-187
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• 2004

Die design to minimize the die wear in the cold forging process is very important as it reduce the production cost and the increase of the production rate. The quantitative estimation fur the die wear is too hard because the die wear is caused by many process variables. So, in this paper, the optimal shape of the backward extrusion punch is newly designed through the FE-analysis considering the surface expansion and Archard wear model in order to reduce the rapid wear rate that is generated for the backward extruded products exceeding the forming limit. The main shape variables of the backward extrusion punch are the flat diameter, angle, and round of the punch nose part. As the flat diameter and angle of the punch nose are larger, the surface expansion is reduced and the wear rate is decreased according to the reduction of the punch round. These results obtained through this study can be applied to the real manufacturing process.