• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.265-277
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• 1998

The control chart has been used widely and importantly as a tool for statistical process control(SPC). Most companies are concerned with improving the quality and the productivity as well as reducing the cost, especially in today's highly competitive environment. Though SPC is known as a technique for consistent quality, it is not used properly due to lack of knowledge about it. It is required to develop a support system for control chart selection and interpretation that can be utilized by non-specialist without hard training or experiences. The support system was developed by applying the expert system tool to popular control charts. Though some researches on this area has been performed, the implemented results expose many problems in field applications due to the unsatisfactory explanation of the selected control chart and limited knowledge base for resolving the problems. This thesis presented an expert system for control chart as solution for these problems. The expert system for the control chart selection and interpretation is developed by using Turbo C and EXSYS which is an expert system development tool.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.372-381
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• 2006

The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.

• Journal of the Korea Fashion and Costume Design Association
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• v.25 no.3
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• pp.143-155
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• 2023

This study examined the effect of beauty service location characteristics and service quality on customer satisfaction and revisit intention, and analyzed the gender control effect of location characteristics and service quality in the relationship with customer satisfaction. The Statistical Package for the Social Sciences (SPSS) was employed and analysis was conducted to investigate how location characteristics and service quality in beauty service encounters effects customer satisfaction. The results of this study are as follows. First, visibility, which is a location characteristic, was found to have a significant effect on customer satisfaction. Second, professionalism and convenience, which are service qualities, were found to have a significant impact on customer satisfaction. Third, customer satisfaction was found to have a significant effect on the intention to revisit. Fourth, as a result of analyzing the gender control effect in the relationship between location characteristics and service quality with customer satisfaction, the gender control effect was found in all factors.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.725-737
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• 2022

In the standard assumption of statistical process monitoring (SPM) under consideration, the in-control region of the control parameter of quality characteristic consists of a single point. However, if small deviations from the ideal situation may not be of practical importance, the parametric space can consist of three regions: In-control, indifference, and out-of-control. In this paper, we propose two exponentially weighted moving average (EWMA) charting procedures applicable to the situation with three parameter regions, and compare the efficiency of the proposed procedures with the Shewhart chart and the cumulative sum (CUSUM) chart.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.373-386
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• 2022

Purpose: This study proposes the application of Six Sigma management innovation method for more systematically enhanced execution of Quality by Design (QbD) activities. QbD requires a deeper understanding of the product and process at the design and development stage of the drug, and it is very important to ensure that no fault is fundamentally generated through thorough process control. Methods: Analyzing the background and specific procedures of quality improvement based on the drug design basis, and analyzing the key contents of each step, we have differentated and common points from the 6 Sigma methodology. We propose a new model of Six Sigma management innovation method suitable for pharmaceutical industry. Results: Regulatory agencies are demanding results from statistical analysis as a scientific basis in developing medicines to treat human life through quality improvement activities based on drug design. By utilizing the education system to improve the statistical analysis capacity in the Six Sigma activities and operating the 6 Sigma Belt system in conjunction, it helped systematically strengthen the execution power of quality improvement activities based on pharmaceutical design based on the members of the pharmaceutical industry. Conclusion: By using QbD Six Sigma, which combines quality enhancement based on pharmaceutical design basis and Six Sigma methodology suitable for pharmaceutical industry, it is possible to obtain satisfactory results both by pharmaceutical companies and regulators by using appropriate statistical analysis methods for preparing scientific evidence data required by regulatory.

• Journal of Environmental Health Sciences
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• v.28 no.3
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• pp.34-38
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• 2002

Statistical analysis between sewage plant operating parameters and the effluent quality was performed. We extracted two factors from principal component analysis of operating parameters and effluent quality from each plant. The total variance of 84.7%, 79.2% was explained by the two factors at SB plant and SC plant, respectively. The factors were identified at SB plant in the following order 1) the oxidation of organic material by aeration basin microbe,2) biomass in aeration basin and at SC plant 1) the oxidation of organic material by aeration basin microbe, 2) thickening of acti-vated sludge. These results suggested that the control of microbial composition might be critical on the improvement of the effluent quality and plant operating efficiency because most of the factors were related with microbes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2505-2510
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• 2009

Semiconductor processes need measurement to confirm where there are problems in quality after progresses manufacturing process. This paper suggests equipment and automatic measure sampling method that control monitoring ratio according to change point occurrence availability of process that is not measure method by the existent simple ratio rate. This paper defines measure section as ailment section, metastable section and stability section by change point standard and create statistical model of each section and developed suitable measure rate model by section. As a result, we have accomplished maximum throughput and minimum sampling number that needs to maintain constant level of quality. Proposed method minimizes load of measure process by brings production quality sophistication and decrease of process badness and lowers measure rate in stable section making perception about problem occurrence quick heightening measure rate at change point occurrence.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.550-557
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• 2005

The multivariate statistical analysis methods, using both multiple linear regression(MLR) and partial least square(PLS), have been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PET) synthesis. On the basis of the set of data including the flow rate of water vapor, the flow rate of EG vapor, the concentration of acid end groups of a product and other operating conditions such as temperature, pressure, reaction times and feed monomer mole ratio, two multi-variable analysis methods have been applied. Their regression and prediction abilities also have been compared. The prediction results are critically compared with the actual plant data and the other mathematical model based results in reliability. This paper shows that PLS method approach can be used for the reasonably accurate prediction of a product quality of a direct esterification reactor in PET synthesis process.

• The Korean Journal of Applied Statistics
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• v.9 no.2
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• pp.55-72
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• 1996

It is essential in modern industry that quality and procuctivity are improved continuously. To accomplish this purpose, quality control must be maintained in all parts of a company. Recently, some tire manufacture companies are beginning to show interest in quality control. They have tried to achive some results through the statistical analysis for the experimental data which has accumulated up to now and then they strive to determine the structural relationship between the design factors in tire construction and tire performance characteristics. The measurement data obtained from the construction engineering is given in multivariate form owing to the various properties found in tire design components as wll as in performance. Also it may be existed the relationship among the multimple response variables. Thus we proposes the use of the biplot graphical display as an analytic tool of data matrices with complex respects. The proposed biplots are also availalbe to understand both the underlying structure of the data and the roles played by the different components. In particular, we consider the matter of how best to use the biplots in the maltivariate analysis of variance and multiple response data. Finally we apply this method to analyze the actual data.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.545-551
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• 2003

Robust design is to identify appropriate settings of control factors that make the system's performance robust to changes in the noise factors that represent the source of variation. In this paper we propose how to simultaneously optimize multiple quality characteristics using the principal component analysis of multivariate statistical analysis. An example is illustrated to compare it with already proposed method.