• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.130-138
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• 1995

Machined graphite/epoxy surfaces were studied by using SEM (Scanning Electron Microscopy), surface profilometry and its analysis to determine suitable surface describing parameters for machined unidirectional and multidirectional laminate composite. The surface roughness and profile are found to be highly depdndent on the fiber layup direction and the measurement direction. It was possible to machine 90 .deg. and -45 .deg. plies due to the adjacent plies, which were holding those plies. It was found that the microgeometrical variations in terms of roughness parameters $R_{a}$ without $D_{y}$(Maximum Damage Depth) region and $D_{y}$are better descriptors of the machined laminate composite surface than commonly used roughness parameters $R_{a}$and $R_{max}$ The characteristics of surface profiles in laminate composite are well represented in CPD (Cumulative Probability Distribution) plot and PPD (Percentage Probability Density) plot. Edge-trimmed multidirectional laminate surfaces are Gaussian and random for profiles measured along the tool movement direction, they are periodic and non-Gaussian in the direction perpendicular to the tool movement.t.ent.t.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.862-865
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• 2000

The object of this paper is to evaluate the surface roughness using the experimental equation of surface roughness, which is developed in turning by an orthogonal array method. $L_9{3^4}$ orthogonal array method, one of fractional factorial design has been used to study effects of main cutting parameters such as cutting speed, feed rate and depth of cut, on the surface roughness. And the analysis of variance (ANOVA)-test has been used to check the significance of cutting parameters. Using the result of ANOVA-test, the experimental equation of surface roughness, which consists of only significant cutting parameter - feed rate, has been developed. The coefficient of determination of this equation is 0.962.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.6 no.3
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• pp.77-84
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• 2007

As a life cycle of product bas shortend, Rapid prototypiug has come into the spotlight to shortened the time of producint a prototype. But It followed in laminated directivity and of the prototype production which is decided the interference which Beccuase of invisible rough surface, some inferences have occured during an assembly hour. Consequently, This study on rapid prototyping has parameter to determine surface roughness and surface form precision of total shape. In order to improve Surface form precision, it can control parameters which are Laminate Height, Layer Hatch Overcue, Fill Cure Depth. In addition, we found the parameter which have a deep effect on surface roughness among various parameters, and then, tried to reduce the interference that can be occurred by surface roughness during assembly.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.519-524
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• 2000

This paper presents a study of surface roughness prediction model by experimental design in turning operation. Regression analysis technique has been used to study the effects of the cutting parameters such as cutting speed, feed and depth of cut on surface roughness. The experiment has been conducted using coated tungsten carbide inserts without cutting fluid. The reliability of the surface roughness model as a function of the cutting parameters has been estimated. The results show that the experimental design used in cutting process is a method to estimate the effects of cutting parameter on surface roughness.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.7
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• pp.37-45
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• 2004

Grinding is a finishing operation of products in various areas. Surface roughness of industrial components obtained in grinding operation is a critical quality measure but is a function of many operating parameters and their interactions. To achieve higher surface roughness and to identify the influence of grinding parameters on surface roughness, it is an ideal situation fer using the design of experiments. This paper presents an successful optimization of grinding conditions and prediction of surface roughness using the design of experiments. From the experimental verification tests, it was observed that this approach was useful as a robust design methodology for grinding operation.

• The Journal of Engineering Geology
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• v.25 no.2
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• pp.165-178
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• 2015

Physical and chemical weathering degrades rock, affecting its structural properties and thus the stability of stone buildings or other structures. Confocal laser scan microscopy (CLSM) is used here to observe temporal changes in the surface roughness of rock samples under simulated accelerated weathering. Samples were pressurized to 50, 55, or 70 MPa using a pressure frame, and subjected to freeze/thaw cycling controlled by a thermostat. The temperature was cycled from -20℃ to 40℃ and back. After each 20 cycles, CLSM was used to assess the change in surface roughness, and roughness factors were calculated to quantify the progression of the surface condition over time. Variations in cross-section line-roughness parameters and surface-roughness parameters were analyzed for specific parts of the sample surfaces at 5× and 50× magnification. The result reveals that the highest and lowest values of the roughness factors are changed according to elapsed time. Freezing/thawing at high pressure caused larger changes in the roughness factor than at low pressure.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.4
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• pp.153-158
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• 2017

When considering the proper function and life cycle length of a product, its surface finish plays an important role. This experimental study was carried out to understand the effect of input factors on surface roughness and how it can be minimized by controlling the input parameters. This experimental work was performed by machining the surface of STD 61 blocks with a surface inclined at $30^{\circ}$ by ball end-milling and optimizing the input parameters using the Taguchi technique. Signal-to-Noise (S/N) ratio and analysis of variance (ANOVA) were applied to find the significance of the input parameters. The optimum level of input parameters to minimize surface roughness was obtained.

• Tribology and Lubricants
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• v.35 no.1
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• pp.16-23
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• 2019

In this study, we aim to analyze the effects of both contact layer properties and surface roughness on contact resistance. The contact has a great influence on performance in terms of electrical conduction and heat transfer. The two biggest factors determining contact resistance are the presence of surface roughness and the surface layer. For this reason we calculated the contact resistance by considering both factors simultaneously. The model of this study to calculate contact resistance is as follows. First, the three representative surface parameters for the GW model are obtained by Nayak's random process. Then, the apparent contact area, real contact area, and contact number of asperities are calculated using the GW model with the surface parameters. The contact resistance of a single surface layer is calculated using Mikic's constriction equation. The total contact resistance is approximated by the parallel connection between the same asperity contact resistances. The results of this study are as follows. The appropriate thickness with reduction effect for contact resistance is determined according to the difference in conductivity between the base layer and surface layer. It was confirmed that the standard deviation of surface roughness has the greatest influence on surface roughness parameters. The results of this study will be useful for selecting the surface material and surface roughness when the design considering the contact resistance is needed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.2
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• pp.11-18
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• 2008

Surface roughness is an important factor to evaluate machined parts in precision machining. This is the major measure of surface quality. This research sets up experiments to select the factors which affect surface roughness in the machining of inclined planes of aluminum. The levels of the selected experimental factors are chosen to evaluate the relationship between the surface roughness of the machined parts and machining parameters. This is to find out the optimal machining condition in the inclined planes. The objective of this research is to improve the surface roughness of the machined products by using the ANOVA analysis. The factors for the experiments are cutting speed, feed rate, cutting depth, and cutting width. The experimental levels of the factors are two for the cutting depth and width. For the cutting speed and feed rate, their levels are three because they are more sensitive for the surface roughness than the other two. The inclined planes are machined by 5-axis machining equipment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.2
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• pp.37-43
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• 2004

High speed machining is a machining process which cuts materials with the fast movement and rotation of a spindle in a machine tool. It reduces machining time because of the high feed and the high speed of a spindle. In addition it gets rid of post processes for high precision machining. When the high speed machining is applied to especially hardened steel, operators should select the proper parameters of machining. This can produce machining surfaces which is qualified with good surface roughness. This paper presents a method for selecting machining parameters to minimize surface roughness with high speed machining in cutting the hardened steels. Experimental data for surface roughness are collected in a machining shop based on the cutting feed and the spindle rotation. The data fits in hi-cubic polynomial surface of mathematical form. From the model this research minimize the surface roughness to find the optimal values of the feed and the spindle speed. This paper presents a program which automatically generates optimal solutions from the raw data of experiments.