• Economic and Environmental Geology
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• v.27 no.1
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• pp.81-91
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• 1994

There has been considerable research dealing with the influence of surface roughness along surfaces of rock discontinuities in relation to the peak shear strength of rock masses. Concepts accepted recently for measuring such strength include estimation of a roughness coefficient such as developed by Barton's studies. The method for estimation the Joint Roughness Coefficient (JRC) value of a measured roughness profile is subjective. The aim of this research is to estimate the JRC value of the roughness of a surface profile in a rock mass system using an objective method. The study of roughness of surfaces has included measurement of fractal geometric characteristics. Once the irregularity of the surface has been described by the fractal dimension, the spatial variation of the surface irregularities can be described using variogram and drift analysis. An empirical relationships between the roughness profiles of selected JRC ranges and their fractal dimension with variogram and drift were derived. The application of analyses of fractal dimension, variogram and drift was novel for the analysis of roughness profiles. Also, an empirical equation was applied to experimental data.

• Journal of the Korean institute of surface engineering
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• v.43 no.3
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• pp.159-164
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• 2010

Surface roughness of deposited or etched film strongly depends on ion bombardment. Relationships between ion bombardment variables and surface roughness are too complicated to model analytically. To overcome this, an empirical neural network model was constructed and applied to a deposition process of silicon nitride (SiN) films. The films were deposited by using a pulsed plasma enhanced chemical vapor deposition system in $SiH_4$-$NH_4$ plasma. Radio frequency source power and duty ratio were varied in the range of 200-800 W and 40-100%. A total of 20 experiments were conducted. A non-invasive ion energy analyzer was used to collect ion energy distribution. The diagnostic variables examined include high (or) low ion energy and high (or low) ion energy flux. Mean surface roughness was measured by using atomic force microscopy. A neural network model relating the diagnostic variables to the surface roughness was constructed and its prediction performance was optimized by using a genetic algorithm. The optimized model yielded an improved performance of about 58% over statistical regression model. The model revealed very interesting features useful for optimization of surface roughness. This includes a reduction in surface roughness either by an increase in ion energy flux at lower ion energy or by an increase in higher ion energy at lower ion energy flux.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.1 no.1
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• pp.133-140
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• 2002

The object of this paper is to predict the surface roughness using the experiment equation of surface roughness, which is developed with a full factorial design in turning. $3^3$ full factorial design has been used to study main and interaction effects of main cutting parameters such as cutting speed, feed rate, and depth of cut, on surface roughness. For prediction of surface roughness, the arithmetic average (Ra) is used, and stepwise regression has been used to check the significance of all effects of cutting parameters. Using the result of these, the experimental equation of surface roughness, which consists of significant effects of cutting parameters, has been developed. The coefficient of determination of this equation is 0.9908. And the prediction ability of this equation was verified by additional experiments. The result of that, the coefficient of determination is 0.9718.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.248-253
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• 1997

The ideal surface roughness is obtained by tool geometry and feed rate in face milling. however actual surface roughness is affected by various factors such as cutting conditions. vibration and used tool. To improve the quality and productivity of the machining parts, lots of research on the evaluation of tool life and control of surface roughness has been required. Therefore, the width of flank wear, cutting force, and surface roughness are monitored to analyse the characteristics of surface roughness. This experimental investigation is mainly focused on the characteristics of surface roughness in multi-insert milling using TiN coated tool.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.5
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• pp.61-66
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• 2022

This study investigated the characteristics and relationship between surface roughness and vibration according to the cutting conditions in the slot milling of AL7075-T6. The spindle speed, feed, and depth of cut were selected as independent variables and the amplitude of acceleration and surface roughness as dependent variables. Feed affected the surface roughness. As the spindle speed increased, the amplitude of vibration increased in the direction perpendicular to the feed direction. In addition, the amplitude of vibration and surface roughness showed a negative correlation. Under a given feed, the surface roughness improved as the vibration increased.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.7
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• pp.77-82
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• 2019

In this study, we study the change of surface roughness during cutting machining by changing the cutting conditions such as feed rate and spindle velocity with chromium molybdenum steel (SCM415) material and TiCN and TiAlN coated end mill tools. The surface roughness value of the test specimen for SCM415, was found to be 3,000 rpm in TiCN coated end mill and $0.634{\mu}m$ in surface roughness at a feed rate of 100 mm/min. In the TiAlN coated end mill, 300 mm/min, the surface roughness was the best at $0.699{\mu}m$. The overall average surface roughness of each coating tool was better than that of TiAlN.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.4
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• pp.73-78
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• 2002

This paper presents a neural network approach to estimate the surface roughness by considering the relationship between the polishing operation parameters and the surface roughness. The neural network model predicts the post-machining surface roughness by using several factors such as pre-machining surface roughness, pressure, feed rate, spindle speed, and the number of polishing as inputs. In this paper, the several neural network models are implemented to estimate the surface roughness by using actual experimental data. The experimental results show that the neural network approach is more appropriate to represent the polishing characteristics of mold and die compared with the results obtained by the approach using exponential function.

• Journal of Environmental Science International
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• v.6 no.6
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• pp.689-696
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• 1997

This paper discussed effect of the surface roughness and the hydrophobicity of support material on the microbial attachment In a rotating biological contactor. The by- drophoblclty of each support material was determined by the measurement of contact angle of water and the surface roughness was measured by the surface roughness In- strument. Microorganisms have well attached on the surface of more hydrophilic support material like Nylon6 than that of the hydrophobic support material like PE. When the relatively hydrophilic surface was roughen, the microbial attachment was increased but when the relatively hydrophobic surface was roughen, the attachment was slightly In- creased because the hydrophobicity of support material was Increased by roughening the hydrophobic surface. Although both variables, the surface hydrophobicity and the surface roughness, have Influenced the microbial attachment, the influence of the surface roughness overruled that of the surface hydrophobicity. Support material whose surfaces were roughened about 1mm, 6mm and 11mm were allowed for attached 3, 7 and 24hr, but the differences of maximum and minimum attachment of each material gave nearly constant values and similar trend with time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.464-468
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• 1996

To evaluate the surface integrity of machined products such as die, the surface roughness measurement is being much used. Especially, for machining automation and promotion of productivity, the surface roughness measurement technique changes from sepatate measuring system after machining process to the on-the-machine measurement. This study is on the surface roughness measurement by using scattered lights for on-the-machine measurement. This system is designed with a simple optical construction. And experiments are implemented with standard roughness specimen to obtain the parameters which are specularly reflected region parameter and diffusely reflected region parameter. To determine the surface roughness quickly, neural network is used. And this system gives the possibility to apply to the various production processes.

• 대한공업교육학회지
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• v.34 no.2
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• pp.346-362
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• 2009

The purpose of this study was to develop the handy non-contact measurement device of the surface roughness by using the optical fiber sensor. The advantages of fiber optic sensors are high-speed responsibility, non-effect of the magnetic, convenience of the product and high precision. The measurement theory for surface roughness of optical fiber sensor is one to one correspondence between the reflected light intensity based on the surface roughness of the object and the measurement value of previously known for surface roughness. The reflected light intensity was determined using the distance to the surface from the sensor probe and the limit reflection angle based on the surface roughness. Therefore, in this study, the sensor probe was produced for determining the value of surface roughness only using the limit reflection angle based on the surface roughness with the fixed distance from the surface. A prototype measurement system was composed of a transmitting part, a receiving part and a signal processing circuit. The materials of standard measurement which was used in this experiment were SM45C, STS303 and Al60. According to the results of this study, approximation surface roughness formulas which was deduced from the correlation of between the standard surface roughness and the sensing output were verified that they were effect against the surface roughness measurement value of the option sample. And handy optical fiber surface roughness measurement device which was produced by an order was verified that it was effect for measuring of the precision surface roughness.