• 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.12 no.2
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• pp.68-74
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• 2013

In this paper, an average surface roughness, $R_a$, was measured by optical measurement and its mathematical model according to spindle speed and feedrate was obtained by least square method. Also, its result is compared and investigated with real measured average surface roughness. The optical measurement of surface roughness is performed by CLSM(confocal laser scanning microscope) and the captured HEI(height encoded image) data is used as an original data for the generation of average surface roughness and its mathematical plane or contour surface of surface roughness. Using this polynomial model with two independent variables, the behavior of an average surface roughness is investigated and analyzed with an experimental modeling of least square algorithm. And it can be used for the prediction of $R_a$ in different condition of machining.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.230-235
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• 2000

In this paper, a surface generation model is presented to simulate surface roughness profile in turning operation. The simulation model takes into account the effect of tool geometry, process parameters, rotational errors of spindle, and the relative vibration between the cutting tool and workpiece. The surface roughness profiles are simulated based on the surface-shaping system. The model has been verified by comparing the experimental values with the simulation values. It is shown that the surface simulation model can properly predict the surface roughness profile.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.427-436
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• 2008

This paper presents radar remote sensing of soil moisture and surface roughness for vegetated surfaces. A precise volume scattering model for a vegetated surface is derived based on the first-order radiative transfer technique. At first, the scattering mechanisms of the scattering model are analyzed for various conditions of the vegetation canopies. Then, the scattering model is simplified step by step for developing an appropriate inversion algorithm. For verifying the scattering model and the inversion algorithm, the polarimetric backscattering coefficients at 1.85 GHz, as well as the ground truth data, of a tall-grass field are measured for various soil moisture conditions. The genetic algorithm is employed in the inversion algorithm for retrieving soil moisture and surface roughness from the radar measurements. It is found that the scattering model agrees quite well with the measurements. It is also found that the retrieved soil moisture and surface roughness parameters agree well with the field-measured ground truth data.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.2
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• pp.89-94
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• 2001

This paper presents a study of surface roughness prediction model by orthogonal design in turning operation. Regression analysis technique has been used to study the effects of the cutting parameters such as cutting speed, feed depth of cut, and nose radius on surface roughness. An effect of interaction between two parameters on surface roughness has also been investigated. 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 turning process is a method to estimate the effects of cutting parameters on sur-face roughness.

• Atmosphere
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• v.26 no.1
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• pp.111-126
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• 2016

In the numerical weather model, surface properties can be defined by various parameters such as terrain height, landuse, surface albedo, soil moisture, surface emissivity, roughness length and so on. And these parameters need to be improved in the Seoul metropolitan area that established high-rise and complex buildings by urbanization at a recent time. The surface roughness length map is developed from digital elevation model (DEM) and it is implemented to the high-resolution numerical weather (WISE-WRF) model. Simulated results from WISE-WRF model are analyzed the relationship between meteorological variables to changes in the surface roughness length. Friction speed and wind speed are improved with various surface roughness in urban, these variables affected to temperature and relative humidity and hence the surface roughness length will affect to the precipitation and Planetary Boundary Layer (PBL) height. When surface variables by the WISE-WRF model are validated with Automatic Weather System (AWS) observations, NEW experiment is able to simulate more accurate than ORG experiment in temperature and wind speed. Especially, wind speed is overestimated over $2.5m\;s^{-1}$ on some AWS stations in Seoul and surrounding area but it improved with positive correlation and Root Mean Square Error (RMSE) below $2.5m\;s^{-1}$ in whole area. There are close relationship between surface roughness length and wind speed, and the change of surface variables lead to the change of location and duration of precipitation. As a result, the accuracy of WISE-WRF model is improved with the new surface roughness length retrieved from DEM, and its surface roughness length is important role in the high-resolution WISE-WRF model. By the way, the result in this study need various validation from retrieved the surface roughness length to numerical weather model simulations with observation data.

• 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.

• Transactions of Materials Processing
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• v.10 no.7
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• pp.543-550
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• 2001

In order to find the effect of material property and lubricant viscosity on the frictional characteristics a sheet metal friction tester was designed and tensile test, surface roughness test, and friction test were performed with several kinds of drawing oils. Test results show that as the lubricant viscosity becomes lower, the friction coefficient is higher. When surface roughness is extremely low or high, friction coefficient is also high. Using these test results, the friction model considering lubricant viscosity and surface roughness is developed. The validity and accuracy of the friction model are shown by comparing the punch loads among FEM analysis results employing current friction model and conventional friction model respectively and experimental measurement.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.48-54
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• 2007

Surface of rapid prototype has inevitably stair-stepping error, which is attributed to the continuous building process of 2 dimensional area. In this study, rounded edge model was established to estimate the roughness of inclined surface which has stair-stepping error. To investigate the roughness of rapid prototypes, specimens that have various surface inclinations were manufactured by various types of RP machines. As the surface inclination increased, the roughness of the specimens manufactured by SL, FDM, or LOM process decreased, which coincides with the simulation results. However, surface roughness of 3DP specimen was almost independent of the inclination. Furthermore, as the angle of surface increased, roughness of poly-jet specimen also increased, which is attributed to the frictional behavior between writing head and scanned area.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.149-156
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• 1995

This paper presents the surface profile of machined workpiece in face milling operation. The roughness model of feed direction is considered the cutting condition, the profile and run-out of inserts. For the dynamic model the cutting system can be modeled as avibratory system. The dynamic model of surface roughness is considered the relative displacements between tool and work- piece which can be obtained from the cutting system. These model can predict various surface roughnesses. i.e. maximum and arithmetic mean surface ruughnesses. Therefore, the developed model can be used for the monitoring of surface roughness.