• Journal of Korean Society for Quality Management
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• v.30 no.1
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• pp.97-117
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• 2002

We apply Taguchi method to the process optimization problem of chemical reaction process, and some case studies are done to find out the way for cost reduction and quality improvement The parameter and tolerance designs of Taguchi mettled are done with operation data of a chemical process and we propose a procedure how to use and analyze the operation data to find the optimal process conditions and tolerance limits. In order to use the continuous values in experiment conditions, it is suggested how to determine the interval of each level by discrete values and to treat any missing values caused from discrete 4 levels.

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.203-209
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• 2011

This study develops three new models that are practically applicable to three stages of Taguchi's robust design, which includes system design, parameter design and tolerance design. In system design, the Multiple Loss Function Analysis(MLFA) and Overall Loss Index(OLI) which reflect upon weight of characteristics and importance of specification are developed. Moreover parameter design presents Process Capability Index(PCI), $C_{PUK}$ and $C_{PLK}$, in order to segregate Signal-To-Noise Ratio(SNR) into accuracy and precision for an evaluation of relative comparison. In addition, tolerance design presents the new model of allowance computation for assembled product which is primarily derived from safety margin(SM) considering functional limit and specification. The guideline of those three new models, which include systematic charts and applicable illustrations, offers convenience for practitioners in the field. Hence, the practical applications could be made with the steps of robust designs such as system design, parameter design and specification allowance design.

• Journal of Korean Society for Quality Management
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• v.30 no.4
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• pp.58-67
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• 2002

Since the inception of off-line quality control, it has drawn a particular attention from research community and it has been implemented in a wide variety of industries mainly due to its extensive applicability to numerous real situations. Emphasizing design issues rather than control issues related to manufacturing processes, off-line quality control has been recognized as a cost-effective approach to quality improvement. It mainly consists of three design stages: system design, parameter design, and tolerance design which are implemented in a sequential manner. Utilizing experimental designs and optimization techniques, off-line quality control is aimed at achieving product performance insensitive to external noises by reducing process variability. In spite of its conceptual soundness and practical significance, however, off-line quality control has also been criticized to a great extent due to its heuristic nature of investigation. In addition, it has also been pointed out that the process optimization procedures are inefficient. To enhance the current practice of off-line quality control, this study proposes an integrated optimization model by utilizing a well-established statistical tool, so called response surface methodology (RSM), and a tolerance - cost relationship.