• Journal of Korea Foundry Society
• /
• v.24 no.2
• /
• pp.63-70
• /
• 2004

Foundry techniquews for large steel casting depends on the skills of foundrymen considerably. Especially, the problem of reducing casring surface defects is difficult to clear numerically. Statistical analysis by using wuantification theory for hot tear and sand inclusion, and multiple regression analysis for dimensional defects have been shown to be examples of solving this difficulty. Many causes of surface defects can be evaluated by these analyses. These evaluations serve as the base data of defect reduction and contribute to the constant improvement of casting quality and quality enhancement activity. The system to perform quality enhancement activity was developed and it proved very useful for transfering foundry techniques and skills from the old to young generations.

• Journal of Information Technology Application
• /
• v.3 no.4
• /
• pp.119-134
• /
• 2001

Improving software product quality is a key to increasing user satisfaction and to achieving competitive edge. There are two approaches to assure high software product quality; development process-oriented and product-oriented. There have been many efforts for improving software quality through process certification, for example, CMM, ISO 9000 family, ISO/IEC 12207, SPICE and Bootstrap. However, a good process alone cannot guarantee good product quality. A need for the evaluation of software product quality by an independent third party is growing rapidly for several reasons. We are concerned with an application of Statistical Quality Control (SQC) to the evaluation of software product quality to obtain the efficiency of evaluation processes and the objectivity of evaluation results. Methods for selecting test cases using a random sampling approach have been discussed and methods for selecting acceptance criteria with respect to software product quality have also been suggested.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.1
• /
• pp.74-80
• /
• 2021

This study investigated the process quality level of a K5 gas mask, which recently acquired its operational capability, through statistical process analysis for the mass production stages and their lots. The tensile adhesion strength was the only operating requirement in the manufacturing process of the K5 gas mask. For this purpose, the results of tensile adhesion strength between the lens and facial rubber during the initial and second mass production stages were analyzed using conventional statistical and statistical process analysis methods. The conventional statistical results indicated that the second mass production stage was better than the initial mass production stage. In cases of a control chart and process capability of tensile adhesion strength, the process quality level was also improved by following the mass production stages. The improvement was caused by process stabilization and work skill elevation. These results and methods are expected to be conducted and utilized in the third mass production stage. Moreover, quality improvement of K5 gas mask mass production can be achieved using the Lean 6 sigma procedure, MDAIC (Define, Measure, Analyze, Improve, Control).

• Journal of the Korean Statistical Society
• /
• v.33 no.4
• /
• pp.381-399
• /
• 2004

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for process quality improvement. SPC re-duces process variability by detecting and eliminating special causes of process variation, while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been need for an integrated process control (IPC) procedure which combines the two strategies. This paper considers a scheme that simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an IMA(1,1) model with a step shift. The EPC part of the scheme adjusts the process, while the SPC part of the scheme detects the occurrence of a special cause. For adjusting the process repeated adjustment is applied according to the predicted deviation from target. For detecting special causes the exponentially weighted moving average control chart is applied to the observed deviations. It was assumed that the adjustment under the presence of a special cause may increase the process variability or change the system gain. Reasonable choices of parameters for the IPC procedure are considered in the context of the mean squared deviation as well as the average run length.

• Journal of the Korea Safety Management & Science
• /
• v.15 no.2
• /
• pp.243-253
• /
• 2013

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

• Journal of Korean Society for Quality Management
• /
• v.32 no.2
• /
• pp.191-200
• /
• 2004

Using the p chart is not adequate in case that there are lots of data and it is difficult to divide into products conforming or nonconforming because of obscurity of binary classification. So we need to design a new control chart which represents obscure situation efficiently. This study deals with the method to performing arithmetic operation representing fuzzy data into fuzzy set by applying fuzzy set theory and designs a new control chart taking account of a concept of classification on the term set and membership function associated with term set.

• Journal of Korean Institute of Industrial Engineers
• /
• v.7 no.2
• /
• pp.55-59
• /
• 1981

An Application of Experimental Designs to Improve the quality of Industrial product : optimization Methodology of statistical model. The primary object of this paper is to aid scientists and Engineers, in applying response surface procedures to obtain operating conditions for many technical fields, particularly for industrial manufacturing processes. The problem considered in this paper is to select technically and scientifically some important factors affecting the quality of products through the experimental design and analysis of response surface. Even though the mathematical model is unknown these statistical analysis can be applicable to control the quality of industrial products and to determine optimum operating conditions for many technical fields, particularly, for industrial manufacturing processes. This paper proposes a method to obtain the optimum operating condition, and how to find the condition by using table of orthogonal array experiments, and optimization methodology of statistical model.

• Journal of Korean Society for Quality Management
• /
• v.44 no.2
• /
• pp.221-244
• /
• 2016

Purpose: This research reviews the papers, published in the Journal of the Korean Society for Quality Control (KSQC) and the Journal of the Korean Society for Quality Management (KSQM) since 1965, in the area of statistical methods. The literature review is performed in the four fields of the statistical methods and we categorize the published articles into the several sub-areas in each field. Methods: The reviewed articles are classified into the four main categories: probability model and estimation, Bayesian analysis and non-parametric analysis, regression and time series analysis, and application of data analysis. We examine the contents and relationships of the published articles of the several sub-areas in each category. Results: We summarize the reviewed papers in the chronological road-maps for each sub-area, and outline the relations of the connected papers. Some comments on the contents and the contributions of the reviewed papers are also provided in this paper. Conclusion: Various issues are employed and published on the research of the application statistical methods for past 50 years, and many worthy works are achieved in the theory and application areas of statistical methods for improving quality in the manufacturing and service industries. The future direction of the research in the statistical quality management methods also can be explored by the contents of this research.

• Journal of Korean Society for Quality Management
• /
• v.5 no.2
• /
• pp.3-20
• /
• 1977

The purpose of this Study is to develope the Role of Statistics in Industrial mass production. The process of mass production will be divided into three steps, that is, Standardization, production and inspection. The Statistics is applied to Specificat-ions, Quality Control and Sampling inspection in these three steps. The applications have developed to Statistical methods based on probability theory. And then, The improved plan is exhibited the point of problems of introducting of spreading of quality control throughout field survey.

• Proceedings of the Korean Society for Quality Management Conference
• /
• 2007.04a
• /
• pp.302-309
• /
• 2007

To conduct statistical process control needs the assumption that the process data are independent. However, most of chemical processes, like a semi-conduct processes do not satisfy the assumption because of autocorrelation. It causes abnormal out of control signal in the process control and misleading process capability. In this study, we introduce that Shore's method to solve the problem and to find the optimal subgroup size to estimate variance for AR(l) model. Especially, we focus on finding an actual subgroup size for small samples using simulation. It may be very useful for statistical process control to analyze process capability and to make a Shewhart chart properly.