• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.46-50
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• 2018

The purpose of this study is to analyze effects of filtering system of cutting fluid which is used for machining silicon electrode. For the research, different sizes of filter clothes are applied to check grain size of sludge of cutting fluid. Surface roughness of machined workpiece, depth of damage inside of silicon electrode, and suspended solids of cutting fluid are experimented and analyzed. From these experiments, it is verified that filtering system of cutting fluid is very important factor for machining. Results of this study can affect various benefits to the semiconductor industry for better productivity and better atmospheric pollution in workplace.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1352-1355
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• 1996

The control of diamond turning is usually achieved through a laser-interferometer feedback of slide position. If the tool post is rigid and the material removal process is relatively static, then such a non-collocated position feedback control scheme may surface. However, as the accuracy requirement gets tighter and desired surface contours become more complex, the need for a direct tool-tip sensing becomes inevitable. The physical constraints of the machining process prohibit any reasonable implementation of a tool-tip motion measurement. It is proposed that the measured force normal to the face of the workpiece can be filtered through an appropriate admittance transfer function to result in the estimated depth of cut. This can be compared to the desired depth of cut to generate the adjustment control action in addition to position feedback control. In this work, the design methodology on the admittance model-based control with a conventional controller is presented. The recursive least-squares algorithm with forgetting factor is proposed to identify the parameters and update the cutting process in real time. The normal cutting forces are measured to identify the cutting dynamics in the real diamond turning process using the precision dynamometer. Based on the parameter estimation of cutting dynamics and the admittance model-based nanodynamic control scheme, simulation results are shown.

• 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.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.192-197
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• 2002

In this study, examined the cutting characteristics of alumuminum alloy AC8A-T6 that is used to present car piston materials. And in been holding materials machining empirically escape as result that experiment comparison changing the cutting speed and feed on various condition to choose efficient machining condition. The following results can be summarized from this research. 1. As the cutting speed decreased, principal cutting force and thrust cutting force is increased, and reason that cutting force interacts greatly in the low cutting speed is thought by result by BUE's stabilization. 2. The feed speed and cutting speed increase, friction factor is decrescent and the cause appeared the thrust cutting force is fallen than cutting force relatively because chip flow according to increase of the feed rate is constraint. 3. Though specific cutting resistance grows cutting area and the feed rate are few, the cause was expose that shear angle decreases by rake face of tool gets into negative angle remarkably as wear of a cutting tool or defect part of workpiece is cut. 4. Cutting speed do greatly depth of cut is slow, surface roughness examined closely through an experiment that becomes bad, and know that it can get good surface that process cutting speed because do feed rate by 0.1mm/rev low more than 250m/min to get good surface roughness can.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.59-63
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• 2021

Upper electrode is one of core parts using plasma etching process at semiconductor. The purpose of this study is to analyze effects of cutting conditions for mechanical machining of silicon upper electrode. For this research, surface roughness of machined workpiece and depth of damage inside of silicon electrode are experimented and analyzed and different values of feed rate and depth of cut are applied for the experiments. From these experiments, it is verified that the surface roughness and internal damaged layer get worse according to take more fast feed rate. In conclusion, cutting condition is very important factor for machining. Results of this study can use to develop various parts which are made from single crystal silicon and affect various benefits to the semiconductor industry for better productivity.

• Structural Engineering and Mechanics
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• v.66 no.3
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• pp.285-294
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• 2018

An experimental study has been carried out to analyze the effect of cutting parameters (cutting speed, feed and depth of cut) and tool nose radius on the surface roughness and the cutting force components during hard turning of the AISI 52100 (50 HRC) steel with a ceramic cutting tool. The tests have been conducted according to the methodology of planning experiments, based on an orthogonal plan of Taguchi (L27). By using the response surface methodology (RSM), the components of the cutting force and the roughness of the machined surface were modeled and the effects of the input parameters were analyzed statistically by ANOVA and RSM. The results show that the feed (f), the tool nose radius (r), the cutting speed (Vc), the interaction between feed and tool nose radius ($f{\times}r$) as well as that of the quadratic effect ($f^2$) all have significant effects on the surface roughness (Ra). The feed is the most influencing factor with a contribution of 47.31%. The components of the cutting force were strongly influenced by the depth of cut, followed by the advance with a lower degree. By comparing the experimental values with those predicted by the models of the cutting force components and the surface roughness, it appears that they are in very good correlation.

• 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.

• Progress in Medical Physics
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• v.13 no.4
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• pp.234-241
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• 2002

The depth electrode technique, especially using the occipito-temporal route is widely used in the clinic, since it is known to be the most precise method about the invasive study of the mesial temporal lobe epilepsy. The depth electrode with the occipito-temporal route has been applied with manual calculation of MR images. Inherently there are some factor causing significant errors due to geometrical axis alignment, such as miss alignment of horizontal or vertical cutting line. In order to correct it in manually, it takes a long time. In this study, authors has developed the software for automate calculation of MRI wording for instance calculation and for the no time delaying operation. Authors could show that this software is useful for the clinic after applying if for 33 cases of the patients.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.386-396
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• 2022

To analyze the influence of variables of roadheaders, the linear cutting testing data of pick cutter were collected from the former literatures. The input factors were set up as uniaxial compressive strength, cutting depth, cutting spacing, attack angle, skew angle, and output factors were determined as specific energy, average cutting force, maximum cutting force, average vertical force, and maximum vertical force. After composing a table of the design of experiment (DOE). The contribution level of each factor was calculated by analysis of variance (ANOVA). As a result, the factors having greatest influence on cutting force and specific energy were uniaxial compressive strength and cutting spacing.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.1038-1043
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• 1997

To adapt the neural network proess for the purpose of determination of optimal utting onditions (optimal cutting speed and feed rate), some selection strategies for the machining factors are necessary, which is considered planning cutting process. In this case, factors that have both nonlinearity and strong relationship must be selected. Although tool grade and chemical properties of workpiece material have strong effect to cutting speed, it's not easy to find a analytic relation between them. In this paper, a mathematical method for determining the optimal amount of cutting (depth of cut, feed rate) is presented by tool goemetry and heat generation during cutting process. And various tool grade and workpiece material groups ase classified based on its chemical properties. Thier chemical composition and hardness are used as input pattern for neural network learnig. The result of learning shows the relationship between tool grade and workpiece material and it is proved that it can be used as a sub-system for automatic process planning system.