• 품질경영학회지
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• 제25권2호
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• pp.140-153
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• 1997

As product quality is maily determined in the product design and process design step, systematic design should be performed through parameter design and tolerance design. Therefore, we introduced analysis of variance and regression analysis as a statistical method which determine optimal levels of affective design factors to product characteristics, then we compared that process and result. In analysis of variance, variation of quality characteristics arises from noise factors, so the optimal levels of design factors are selected to minimize the effect of noise factors. In regression analysis, variation of quality characteristics aries from variation of each own design factors. As a method to reduce variation of these quality characteristics, sensitivity analysis was performed about each design factors. Through this sensitivity analysis, we represented process to calculate the interaction term of the factors.

• 산업경영시스템학회지
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• 제35권3호
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• pp.129-135
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• 2012

Companies strive for quality improvement and use process data obtained through measurement process to monitor and control the process. Measurement data contain variation due to error of operator and instrument. The total variation is sum of product variation and measurement variation. Gage R&R is for repeatability and reproducibility of measurement system. Gage R&R study is usually conducted to analyze the measurement process. In performing the gage R&R study, several parameters such as the appropriate number of operators (o), sample size of parts (p), and replicate (r) are used. In this paper we propose how to determine the optimal combination of number of operators (o), sample size of parts (p), and replicates (r) considering measurement time and cost by statistical method.

• 품질경영학회지
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• 제29권3호
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• pp.166-176
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• 2001

This paper considers a six sigma project for reducing the color variation of the monitor materials in a chemical plant. The project follows a disciplined process of five macro phases: define, measure, analyze, improve, and control (DMAIC). A process map is used to identify process input variables. Three key process input variables are selected by using an input variable evaluation table; a melting pressure, a coloring agent, and a DP color variation. DOE is utilized for finding the optimal process conditions of the three key process input variables. The sigma level of defects rate becomes a 4.58 from a 2.0 at the beginning of the project.

• 대한수학회보
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• 제51권6호
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• pp.1561-1577
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• 2014

In this paper, we define a conditional transform with respect to the Gaussian process, the conditional convolution product and the first variation of functionals via the Gaussian process. We then examine various relationships of the conditional transform with respect to the Gaussian process, the conditional convolution product and the first variation for functionals F in $S_{\alpha}$ [5, 8].

• 산업경영시스템학회지
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• 제39권1호
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• pp.116-122
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• 2016

Recently, the production cycle in manufacturing process has been getting shorter and different types of product have been produced in the same process line. In this case, the control chart using coefficient of variation would be applicable to the process. The theory that random variables are located in the three times distance of the deviation from mean value is applicable to the control chart that monitor the process in the manufacturing line, when the data of process are changed by the type of normal distribution. It is possible to apply to the control chart of coefficient of variation too. ${\bar{x}}$, s estimates that taken in the coefficient of variation have just used all of the data, but the upper control limit, center line and lower control limit have been settled by the effect of abnormal values, so this control chart could be in trouble of detection ability of the assignable value. The purpose of this study was to present the robust control chart than coefficient of variation control chart in the normal process. To perform this research, the location parameter, ${\bar{x_{\alpha}}}$, $s_{\alpha}$ were used. The robust control chart was named Tim-CV control chart. The result of simulation were summarized as follows; First, P values, the probability to get away from control limit, in Trim-CV control chart were larger than CV control chart in the normal process. Second, ARL values, average run length, in Trim-CV control chart were smaller than CV control chart in the normal process. Particularly, the difference of performance of two control charts was so sure when the change of the process was getting to bigger. Therefore, the Trim-CV control chart proposed in this paper would be more efficient tool than CV control chart in small quantity batch production.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.1626-1631
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• 2007

The purpose of the study is to simulate the displacement of the LCD glass during process of a large size imprint. During this process, a small temperature variation makes thermal stress, which causes the horizontal variation of mold and glass. During alignment process to fix the LCD glass on a alignment stage, the vertical displacement is made by the absorption pressure and the shear stress. This study simulates the horizontal displacement of mold and glass due to temperature variation, the vertical displacement depending on the shape of absorption surface fixing the LCD glass in the alignment process, and the horizontal and vertical displacement which occurs in the LCD glass at the alignment process. Algor which is a FEM code for a framework simulation was applied. Temperature variation above ${\pm}$ $0.1^{\circ}C$ on mold and glass causes the horizontal displacement of 150nm due to thermal expansion. The vertical displacement due to the circular is ten times of the case of rectangular absorption nozzle. The displacement of the LCD glass in the alignment process is about 49nm.

• 대한산업공학회지
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• 제36권3호
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• pp.154-163
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• 2010

Process monitoring of output variables affecting final performance have been mainly executed in semiconductor manufacturing process. However, even earlier detection of causes of output variation cannot completely prevent yield loss because a number of wafers after detecting them must be re-processed or cast away. Semiconductor manufacturers have put more attention toward monitoring process inputs to prevent yield loss by early detecting change-point of the process. In the paper, we propose the method to efficiently monitor functional input variables in multi-phase semiconductor manufacturing process. Measured input variables in the multi-phase process tend to be of functional structured form. After data pre-processing for these functional input data, change-point analysis is practiced to the pre-processed data set. If process variation occurs, key variables affecting process variation are selected using contribution plot for monitoring efficiency. To evaluate the propriety of proposed monitoring method, we used real data set in semiconductor manufacturing process. The experiment shows that the proposed method has better performance than previous output monitoring method in terms of fault detection and process monitoring.

• 열처리공학회지
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• 제22권1호
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• pp.16-22
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• 2009

The rolling process is an efficient and economical approach for the manufacturing of plate metals. In the rolling process, the temperature variation is very critical for plate thickness accuracy. The main cause of thickness variation in hot plate mills is the non-uniform temperature distribution along the length of the slab. Also the exit plate thickness is mainly affected by the rolling conditions such as mill modulus, plate thickness and plate width. Hence the thickness variation in top-end is also dependent on these factors. Therefore this study has concentrated on determining the correct amounts of thickness variation due to top-end temperature drop and process parameters.

• 한국전기전자재료학회논문지
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• 제14권3호
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• pp.241-245
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• 2001

In this paper, we present parameter optimization technique for GaAs/AlGaAs multiple quantum well avalanche photodiodes used for image capture mechanism in high-definition system. Even under flawless environment in semiconductor manufacturing process, random variation in process parameters can bring the fluctuation to device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. The precise modeling for this variation is thus required for accurate prediction of device performance. This paper will first use experimental design and neural networks to model the nonlinear relationship between device process parameters and device performance parameters. The derived model was then put into genetic algorithms to acquire optimized device process parameters. From the optimized technique, we can predict device performance before high-volume manufacturign, and also increase production efficiency.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2008년도 춘계학술대회 논문집
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• pp.281-284
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• 2008

Film insert melding is one of the surface processes that enhances functional or aesthetic qualities of an existing product's surface. In general, film insert molding consists of three processes including thermoforming, trimming and injection molding. Thermoforming, which is the first process of film insert molding, is the most important process because the variation of film thickness has an effect on the mold design and process conditions for the subsequent processes, that are, trimming and injection molding. This study is focused on predicting the film thickness distribution through film insert thermoforming process using commercial FEM code. In order to describe rheological behavior of thermoplastic film (ABS), G'Sell's viscoelastic constitutive law was adopted. The numerical model of film insert thermoforming was established, and the simulation to predict film thickness distribution was performed. Comparison between the results of simulation and experiment was made to validate the proposed finite element analysis.