• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.9
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• pp.983-989
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• 2011

A numerical algorithm for predicting fretting wear was developed using the boundary element method (BEM). A contact analysis was performed numerically using the relation between the elastic displacement and uniformly distributed loading of a rectangular patch on a semi-infinite solid. Geometrical updating based on nodal wear depths was performed. The wear depths were computed using the Archard's equation for sliding wear. In order to investigate the efficiency of BEM for predicting fretting wear, a problem involving a two-dimensional cylinder on a flat contact was analyzed, comparing it with the simulation model proposed by McColl et al. that was based on the finite element method. The developed method was then applied to the analysis of a spherical contact and it was shown that the developed simulation technique could efficiently predict fretting wear. Moreover, the effect of a step cycle on the solution obtained by the developed method was investigated.

• Nuclear Engineering and Technology
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• v.42 no.1
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• pp.97-108
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• 2010

Fluid-elastic instability and turbulence-induced vibration of steam generator U-tubes of a nuclear power plant are studied numerically to investigate the effect of design changes of support structures in the upper region of the tubes. Two steam generator models, Model A and Model B, are considered in this study. The main design features of both models are identical except for the conditions of vertical and horizontal support bars. The location and number of vertical and horizontal support bars at the middle of the U-bend region in Model A differs from that of Model B. The stability ratio and the amplitude of turbulence-induced vibration are calculated by a computer program based on the ASME code. The mode shape with a large modal displacement at the upper region of the U-tube is the key parameter related to the fretting wear between the tube and its support structures, such as vertical, horizontal, and diagonal support bars. Therefore, the location and the number of vertical and horizontal support bars have a great influence on the fretting wear mechanism. The variation in the stability ratios for each vibrational mode is compared with respect to Model A and Model B. Even though both models satisfy the design criteria, Model A shows substantial improvements over Model B, particularly in terms of having greater amplitude margins in the turbulence-excited vibration (especially at the inner region of the tube bundle) and better stability ratios for the fluid-elastic instability.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.89-94
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• 1995

A16061, SB41 and SM45C was used for developing tool wear monitoring system in face milling. First of all, Neural networks of which input are 8 $_{th}$ order AR morel parameters, frequency band energies, cutting conditions was used to monitor tool wear for each material. Finally, A unified neural network, which has tensile strengths of each material as an additional input, was constructed to consider the effect three materials on the features of tool wear. It was verified that tensile strength is the one of properties of workpiece materials.s.

• Elastomers and Composites
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• v.58 no.1
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• pp.17-25
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• 2023

Abrasion tests of model tire tread compounds (NR and NR/BR blend compounds) were performed at different slip angles (1° and 7°) using a laboratory abrasion tester. The abrasion behavior was investigated by analyzing the worn surface and wear particles. The abrasion spacing formed on the specimen worn at the large slip angle of 7° was significantly narrower than that at the small slip angle of 1°, while the abrasion depth for the specimen worn at 7° was lower than that at 1°. The abrasion spacing and depth tended to be narrower and lower, respectively, as the BR content increased. The abrasion patterns were clearly visible on the outside of the specimen for the slip angle of 1° but not for 7°. The wear particles had a rough surface and there were numerous micro-bumps. It was found that the crosslink density affected the abrasion patterns and morphologies of the wear particles.

• The Research Journal of the Costume Culture
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• v.19 no.3
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• pp.622-636
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• 2011

The purpose of this study was to assess the effects of self-image congruence on attitudes toward purchase intentions of children's clothing via the Behavioral Intention Model. The empirical study was conducted via on-line survey and data were collected from mothers with children aged 6 to 10 years. A total of 593 respondents answered the questionnaire and 574 usable data were statistically analyzed. SPSS 18.0 was used to conduct descriptive statistical analysis, factor analysis, reliability analysis, cluster analysis, Chi-square test, ANOVA, and multiple regressions. A K-means cluster analysis was conducted based on three dimensions brand images of children's wear. Respondents were divided into four groups: elegant image group, multiple image group, ordinary image group, and childlike image group. Characteristics of consumers and clothing evaluative criteria that mothers considered important differed significantly across groups. Moreover, based on these groups, each dimension of self-congruence had different effects on brand attitude. Brand attitude and subjective norms had different effects on purchase intentions. In conclusion, levels of self-congruence and factors influencing purchase intention varied according to brand images of children's wear.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.497-498
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• 2006

Tribological properties of a Magneto-Rheological(MR) fluid in a polishing process are studied. For this polishing process, abrasive wear model is proposed as a function of shear force, normal force and actual mean velocity of MR particles at workpiece surface. Experimental conditions are changed by varying the gap distance between workpiece and tool and the rotational speed of tool. From the experimental results, a modified Stribeck curve is obtained, and the friction coefficient turns out to have linear relationship with a modified Sommerfeld number. The validity of the wear model is supported by additional experiments performed for measuring material removal rates.

• Transactions of the Society of Information Storage Systems
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• v.5 no.2
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• pp.70-75
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• 2009

Most hard disk drives uses load/unload technology because of benefits as like an increased areal density, a reduced power consumption and an improved shock resistance. However, ramp tilt induced by ramp manufacture and assembly causes mechanical problems such as unload fail in case of exceeding ramp tolerance. In this paper, we focus on experimental analysis for unloading characteristics affected by ramp tilt. We repeatedly perform load/unload test as 500,000 cycles for original model and ramp tilt model. This paper shows that it is possible to analyze unload characteristics through measuring scratch and wear of suspension lift-tab, ramp, suspension dimple-flexure and disk. We also identify structural relation between suspension lift-tab and ramp through scratch and wear of suspension lift-tab and ramp. As the result of measurement and analysis, we can investigate decrease of unloading performance in ramp tilt model.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.138-149
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• 1994

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.3
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• pp.69-80
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• 1993

It is important to prevent roll failure in hot rolling process for reducing maintenance coat and production loss. Roll material and rolling conditions such as the roll force and torque have been intensively investigated to overcome the roll failures. In this study, a computer roll life prediction system under working condition is developed and evaluated on IBM-PC level. The system is composed and fatigue estimation models which are stress analysis, crack propagation, wear and fatigue estimation. Roll damage can be predicted by calculating the stress anplification, crack depth propagation and fatigue level in the roll using this computer model. The developed system is applied to a work roll in actual hot rolling process for reliability evaluation. Roll failures can be diagnosed and the propriety of current working condition can be determined through roll life prediction simulation.

• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.337-348
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• 2016

An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.