• 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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.8
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• pp.93-98
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• 2021

The complex effects of the machining parameters make it is difficult to control and predict surface roughness. The theoretical surface roughness observed during mechanical machining with a round tool is determined by the tool radius and pitch. However, it was revealed that other parameters, such as the depth of cut and cutting speed, also affect surface roughness. This study adapted the Taguchi method, which can analyze the effects of cutting parameters quantitatively with an efficient number of experiments, to optimize the parameters for better surface roughness. Experiments were designed based on an orthogonal array, and the quantitative effects on the surface roughness were analyzed using the S/N ratio. The surface roughness was affected by all parameters, especially the tool radius. The optimum cutting parameter values obtained in this study showed better surface roughness than the other combinations of the parameters.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.251-254
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• 1995

The quality and functionality of machined products is determined by surface finish. The surface roughness is characterized by roughness parameters such as R $_{a}$ and R $_{max}$. While such parameters are useful to define the quality of surface, they are nor sufficiently descriptive characteristics of surface. The fractal dimension which can describe characteristics od surface roughness than conventional roughness parameters has been applied. In this work, Relation between fractal dimension and surface roughness will be examined as a means of characterizing surface roughness.s.s.

• 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.

• Tunnel and Underground Space
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• v.12 no.4
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• pp.268-276
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• 2002

In this study, a 3D coordinate measurement system equipped with a laser displacement meter for digitizing rock joint surface was established and the digitized data were used to calculate several roughness parameters. The parameters used in this study were micro avenge inclination $angle(i_{ave})$, average slope of joint $asperity(SL_{ ave})$, root mean square of $i-angle(i_{rms})$, standard deviation of height(SDH), standard deviation of $i-angle(SD_i)$, roughness profile $index(R_P)$, and fractal dimension(D). The relationships between the roughness parameters based on the digitzation of the surface profile were analyzed. Since the measured value varied according to the degree of reflection and the variation of colors at the measuring point, rock joint surface was painted in white to minimize the influence of the surface conditions. The comparison of the measured values and roughness parameters before and after painting revealed the better consequence from measurement on the painted surfaces. Also, effect of measuring interval was studied. As measured interval was increased, roughness parameters were exponentially decreased. The incremental sequence of degree of decrease was $SDH\; i_{ave},\; i_{rms},\; SD_i,\;and\; R_ p-1$. As a result of comparison of parameters from pin-type measurement system and laser type measurement system, all value of parameters were higher when laser-type measurement system was used, except SDH.

• Proceedings of the KSRS Conference
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• v.1
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• pp.520-523
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• 2006

Whereas it is well known that the surface roughness parameters, the RMS height and the correlation length, of a natural soil surface are underestimated with a short surface profile, it is not clear how much the underestimated surface parameters affect the backscattering coefficients of the surface for various incidence angles and polarizations. The backscattering coefficients of simulated and measured surface profiles are computed using the integral equation method (IEM) and analyzed in this paper to answer this question. It is shown that the RMS error of the backscattering coefficients between 5-m- and 1-m-long measured surface profiles is 1.7 dB for vv-polarization and 0.5 dB for hh-polarization at a medium range of incidence angle ($15^{\circ}{\leq}{\theta}{\leq}70^{\circ}$), while the surface roughness parameters are significantly reduced; from 2.4 cm to 1.5 cm for the RMS height s and from 35.1 cm to 10.0 cm for the autocorrelation length l. This result is verified with numerous simulations with various roughness conditions and various wavelengths.

• Structural Engineering and Mechanics
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• v.70 no.4
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• pp.395-405
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• 2019

In the present work, the effects of cutting parameters on surface roughness parameters (Ra), tool wear parameters (VBmax), tool vibration (Vy) and material removal rate (MRR) during hard turning of AISI 4140 steel using coated carbide tool have been evaluated. The relationships between machining parameters and output variables were modeled using response surface methodology (RSM). Analysis of variance (ANOVA) was performed to quantify the effect of cutting parameters on the studied machining parameters and to check the adequacy of the mathematical model. Additionally, Multi-objective optimization based desirability function was performed to find optimal cutting parameters to minimize surface roughness, and maximize productivity. The experiments were planned as Box Behnken Design (BBD). The results show that feed rate influenced the surface roughness; the cutting speed influenced the tool wear; the feed rate influenced the tool vibration predominantly. According to the microscopic imagery, it was observed that adhesion and abrasion as the major wear mechanism.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.4
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• pp.56-68
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• 2014

This paper presents an experiment on the modeling, analysis, prediction and optimization of machining parameters used during the turning process of the low-carbon steel known as ST40. The parameters used to develop the model are the cutting speed, the feed rate, and the depth of the cut. The experiments were carried out under various conditions, with three level of parameters and two different treatments for each level (with and without a lubricant), to determine the effects of the parameters on the surface roughness and electric current consumption. These effects were investigated using response surface methodology (RSM). A second-order model is used to predict the values of the surface roughness and the electric current consumption from the results of experiments which collected preliminary data. The results of the experiment and the predictions of the surface roughness and electric current consumption under both treatments were found to be nearly identical. This result shows that the feed rate is the main factor that influences the surface roughness and electric current consumption.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.54-59
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• 2004

A measure for good products manufactured by grinding process is the surface roughness that is affected by a lot of operating parameters such as types of abrasive, grain size, bond material, wheel speed, table speed, depth of cut, hardness of workpiece and stiffness of grinding machine. In this study, an application of the design of experiments was tried for evaluating the effect of operating parameters on the surface roughness. The workpiece was a high speed tool steel(SKH51) and the surface grinding was conducted. In order to obtain the best surface roughness within constraints of the working range, the optimal grinding conditions were selected. The usefulness of this method was evaluated by the statistical strategy.

• Structural Engineering and Mechanics
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• v.73 no.5
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• pp.501-513
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• 2020

In the present work, the optimization of machining parameters to achieve the desired technological parameters such as surface roughness, tool radial vibration and material removal rate have been carried out using response surface methodology (RSM). The hard turning of EN19 alloy steel with coated carbide (GC3015) cutting tools was studied. The main problem faced in manufacturer of hard and high precision components is the selection of optimum combination of cutting parameters for achieving required quality of surface finish with maximum production rate. This problem can be solved by development of mathematical model and execution of experiments by RSM. A face centred central composite design (FCCD), which comes under the RSM approach, with cutting parameters (cutting speed, feed rate and depth of cut) was used for statistical analysis. A second-order regression model were developed to correlate the cutting parameters with surface roughness, tool vibration and material removal rate. Consequently, numerical and graphical optimization were performed to obtain the most appropriate cutting parameters to produce the lowest surface roughness with minimal tool vibration and maximum material removal rate using desirability function approach. Finally, confirmation experiments were performed to verify the pertinence of the developed mathematical models.