• Journal of Adhesion and Interface
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• v.21 no.1
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• pp.27-33
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• 2020

Adhesion property of epoxy adhesive was evaluated with different surface roughness of glass fiber reinforced composite (GFRC) and optimized condition of surface roughness was confirmed. Different sizes of alumina (Al2O3) particles were blasted to GFRC to control surface roughness of GFRC using sand blasting method. The surface roughness was measured and quantified via surface roughness tester. Contact angle was measured using four types of different solvents. Surface energies and work of adhesion between epoxy adhesive and GFRCs were calculated with different surface roughness of GFRC. Adhesion property between epoxy adhesive and GFRCs was evaluated using single lap shear test and adhesion property increased with surface roughness of GFRC. The fracture surface of GFRCs was observed to evaluate adhesion property. Finally, the optimized roughness condition of GFRCs was confirmed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.118-124
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• 2012

In this study, we turning mold steel (SKD61) using whisker reinforced ceramic tool (WA1) to get affected factor to surface roughness and regression equation. For this study, we adapt system of experiments. Results are follows; From the analysis of variance, it was found that affected factor to surface roughness was feed rate, cutting speed, depth of cut in order. From multi-regression analysis, we calculated regression equation and the coefficient of determination($R^2$). $R^2$ was 0.978 and It means regression equation is significant. Regression equation means if feed rate increase 0.039mm/rev, surface roughness will increase $0.8391{\mu}m$, if cutting speed increase 50m/min, surface roughness will decrease $0.034{\mu}m$, if depth of cut increase 0.1mm, surface roughness will increase $0.0203{\mu}m$. From the experimental verification, it was confirmed that surface roughness was predictable by system of experiments.

• Journal of Periodontal and Implant Science
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• v.35 no.4
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• pp.839-850
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• 2005

The success of an implant is determined by its integration into the tissue surrounding the biomaterial. Surface roughness is considered to influence the behavior of adherent cells. The aim of this in vitro study was to determine the effect of surface roughness on Saos-2 osteoblast-like cells. Titanium disks blasted with 75 ${/mu}m$ aluminum oxide particles and machined titanium disks were prepared. Saos-2 were plated on the disks at a density of 50,000 cells per well in 48-well dishes. After 1 hour, 1 day, 6 days cell numbers were counted. One day, 6 days after plating, alkaline phosphatase(ALPase) activity was determined. Compared to experimental group, the number of cells was significantly higher on control group. The stimulatory effect of surface roughness on ALPase was more pronounced on the experimental group than on control group. These results demonstrate that surface roughness alters proliferation and differentiation of osteoblasts. The results also suggest that implant surface roughness may play a role in determining phenotypic expression of cells.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.10a
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• pp.243-244
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• 2009

From large to small vessels of the surface is not smooth. and The surface ship has a surface roughness. Because surface roughness increases the surface resistance and heat transfer, be considered when designing a ship that is an important design factor. Due to surface roughness study on flow around and due to changes in flow and turbulence intensity for the ongoing research is conducted. Roughness of the surface ships from the ship by air as well as machines can be widely applied. In this study, the surface roughness of the leisure marine interval, any change will affect the surface flow, area due to surface roughness for boundary-experimental study was performed.

• The KSFM Journal of Fluid Machinery
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• v.13 no.2
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• pp.5-11
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• 2010

The nanofluidics is characterized by a large surface-to-volume ratio, so that the surface properties strongly affect the flow resistance. We present here the results showing that the effect of wetting properties and the surface roughness may considerably reduce the friction of fluid past the boundaries. For a simple fluid flowing over hydrophilic and hydrophobic surfaces, the influences of surface roughness are investigated by the nonequilibrium molecular dynamics (NEMD) simulations. The fluid slip at near a solid surface highly depends on the wall-fluid interaction. For hydrophobic surfaces, apparent fluid slips are observed on smooth and rough surfaces. The solid wall is modeled as a rough atomic sinusoidal wall. The effects on the boundary condition of the roughness characteristics are given by the period and amplitude of the sinusoidal wall. It was found that the slip velocity for wetting conditions at interface decreases with increasing effects of surface roughness. The results show the surface rougheness and wettability determines the slip or no-slip boundary conditions. The surface roughness geometry shows significant effects on the boundary conditions at the interface.

• Tunnel and Underground Space
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• v.21 no.6
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• pp.494-507
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• 2011

Rock joint surface roughness is one of the most important parameters in shear behavior analysis of rock joint surface. Until now, estimation of joint surface roughness has been conducted by various statistical methods with two-dimensional analysis. In this study, standard roughness profile suggested by Barton and Choubey (1977) was expanded into a 3D surface and its surface roughness was analyzed by surface angularity parameter. And the validity of quantification based on surface angularity was secured through comparison with $Z_2$ and Ai parameter. Also the surface angularity parameter was compared with shear strength by joint shear test using the replicated specimen.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1995.03a
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• pp.1-8
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• 1995

This paper proposed a simple method for measuring surface roughness of ground surface. utilizing non-contact in-process measuring system using the diode laser. The measurement system is consisted of a laser unit with a diode laser and a cylindrical lens a detecting unit with polygon mirror and CCD array sensor. and a signal processing unit with a computer and device. During operation, this measuring system can provide information on surface roughness in the measuring distance with a single sampling and simultanilusly monitor the state of the grind wheel. The experimental results, showed that the increase of the feed rate and the dressing speed an caused increase in the surface roughness and when the surface roughness is 4Rmax-10Rmax, the cutting speed is 1653m/min-1665m/min. the feed rate is 0.2m/min-0.9m/min, the dressing speed is 0.2mm/rev-0.4mm/rev, the stylus method and the in-process method can be obtained the same results. thus under limited working conditions. using the proposed system. the surface roughness of the ground surface during cylindrical grinding can be obtained through the in-process measurement method using the diode laser.

• Design & Manufacturing
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• v.17 no.4
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• pp.1-7
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• 2023

In the fabrication of curved multi-display glass for automotive use, the surface roughness of the mold is a critical quality factor. However, the difficulty in detecting micro-cutting signals in a micro-machining environment and the absence of a standardized model for predicting micro-cutting forces make it challenging to intuitively infer the correlation between cutting variables and actual surface roughness under machining conditions. Consequently, current practices heavily rely on machining condition optimization through the utilization of cutting models and experimental research for force prediction. To overcome these limitations, this study employs a surface roughness prediction formula instead of a cutting force prediction model and converts the surface roughness prediction formula into experimental data. Additionally, to account for changes in surface roughness during machining runtime, the theory of position variables has been introduced. By leveraging artificial neural network technology, the accuracy of the surface roughness prediction formula model has improved by 98%. Through the application of artificial neural network technology, the surface roughness prediction formula model, with enhanced accuracy, is anticipated to reliably perform the derivation of optimal machining conditions and the prediction of surface roughness in various machining environments at the analytical stage.

• Tribology and Lubricants
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• v.26 no.6
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• pp.329-335
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• 2010

Recently, the End-milling processing is needed the high-precise technique to get a good surface roughness and rapid time in manufacturing of precision machine parts and electronic parts. The optimum surface roughness has an effect on end-milling working condition such as, cutting direction, spindle speed, feed rate and depth of cut, and so on. It needs to form the correlation of working conditions and surface roughness. Therefore this study was carried out to presume of surface roughness on end-milling working condition of Al7075 by regression analysis. The results was shown that the coefficient of determination($R^2$) of regression equation had a fine reliability of 87.5% and nonlinear regression equation of surface rough was made by multiple regression analysis.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.3
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• pp.41-47
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• 2012

Surface roughness is widely used as an index for processing degree of accuracy. Recently, regression analysis to predict the machining results are actively used to characterize a cutting operations. In the past, diamond machining had been used for ultra precision cutting operation, but now industrial diamond tools like PCD(Polycrystaline Diamond) has been widely used in ultraprecision machining of nonferrous metals. In this study, the authors focus on the effect of PCD tool property on the surface roughness of different types of aluminum alloy after cutting process by CNC operated lathe. Based on the regression analysis approach on a surface roughness data obtained by experiment, predictive analysis of surface roughness is effective to achieve better surface quality.