• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.759-766
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• 2000

In order to design fractional factorial experiments which include some debarred combinations, we should select defining contrasts so that those combinations are to be excluded. Choi(1999) presented a method of selectign defining contrasts to construct orthogonal 3-level fractional factorial experiments which exclude some debarred combinations. In this paper, we extend Choi's method to general p-level fractional factorial experiments to select defining contrasts which cold exclude some debarred combinations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.927-928
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• 2013

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.1 no.1
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• pp.133-140
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• 2002

The object of this paper is to predict the surface roughness using the experiment equation of surface roughness, which is developed with a full factorial design in turning. $3^3$ full factorial design has been used to study main and interaction effects of main cutting parameters such as cutting speed, feed rate, and depth of cut, on surface roughness. For prediction of surface roughness, the arithmetic average (Ra) is used, and stepwise regression has been used to check the significance of all effects of cutting parameters. Using the result of these, the experimental equation of surface roughness, which consists of significant effects of cutting parameters, has been developed. The coefficient of determination of this equation is 0.9908. And the prediction ability of this equation was verified by additional experiments. The result of that, the coefficient of determination is 0.9718.

• Journal of Korean Society for Quality Management
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• v.45 no.4
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• pp.889-902
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• 2017

Purpose: The orthogonality and trace optimal properties are desirable for constructing designs of experiments. This article focuses on the determination of the sizes of experiments for the balanced trace optimal resolution-V fractional factorial designs for 2-level factorial designs, which have near-orthogonal properties. Methods: In this paper, first we introduce the trace optimal $2^t$ fractional factorial designs for $4{\leq}t{\leq}7$, by exploiting the partially balanced array for various cases of experimental sizes. Moreover some orthogonality criteria are also suggested with which the degree of the orthogonality of the designs can be evaluated. And we appraise the orthogonal properties of the introduced designs from various aspects. Results: We evaluate the orthogonal properties for the various experimental sizes of the balanced trace optimal resolution-V fractional factorial designs of the 2-level factorials in which each factor has two levels. And the near-orthogonal 2-level balanced trace optimal resolution-V fractional factorial designs are suggested, which have adequate sizes of experiments. Conclusion: We can construct the trace optimal $2^t$ fractional factorial designs for $4{\leq}t{\leq}7$ by exploiting the results suggested in this paper, which have near-orthogonal property and appropriate experimental sizes. The suggested designs can be employed usefully especially when we intend to analyze both the main effects and two factor interactions of the 2-level factorial experiments.

• Proceedings of the KWS Conference
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• 2003.05a
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• pp.172-175
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. According to the conventional full factorial design, in order to find the optimal welding conditions, 16,384 experiments must be performed. The genetic algorithm however, found the near optimal welding conditions from less than 60 experiments.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.4
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• pp.352-365
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• 2001

Two-level factorial designs are popular in industry due to their simplicity, efficiency, graphical interpretation, and flexibility in sequential experimentation. However, experimenters are often frustrated when they have factors with more than two levels. There have been some works on design of experiments with two- and four-level factors, which mostly deal with qualitative four-level factors. This paper discusses differences between qualitative and quantitative four-level factors. Optimal designs are provided for some designs with four-level quantitative and two-level factors.

• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.31-45
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• 2021

Purpose: A noncentral composite design method is to be developed to explore farther region for the first factorial design. A general guideline for sequential experimentation is provided. Methods: (1) A non-overlapping noncentral composite design (NNCD) is developed, in which the second factorial design shares one design point that indicates the best response value in the first factorial design. (2) Four composite designs are compared in terms of the four design evaluation criteria, which are D-, A, G, and I-optimality. (3) A follow-up design strategy is suggested based on the interaction effect, direction of improvement, number of factors. Results: (1) NNCD and model building method are presented, which is useful for exploring farther region from first factorial design block. (2) The performances of the four composite designs are compared. (3) A follow-up design strategy is suggested. Conclusion: (1) NNCD will be useful to explore farther region for the first factorial design. (2) A follow-up design strategy can be beneficial to the experimental practitioners for product and process design and improvement.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.695-706
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• 1999

When fractional factorial experiments contain some infeasible treatment combinations called debarred combinations we should construct experimental designs so that those debarred combinations are to be excluded by selecting defining contrasts appropriately. By applying Franklin(1995)'s procedure for selecting defining contrasts to Cheng and Li(1993)'s method this paper presents a method of selecting defining contrasts to construct orthogonal 3-level fractional factorial experiments which exclude some debarred combinations.

• Journal of the Semiconductor & Display Technology
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• v.12 no.1
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• pp.47-51
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• 2013

The screen printing is a process that is widely used in manufacturing process of various fields such as flexible devices, portable multimedia devices, OLED, and the solar cell. The screen printing method has been studied as a method for forming the high precision micro-pattern, making the low-cost manufacturing process and reducing cost through improvement of productivity. It is applicable to deposit and forming the pillars which are one of the core element for comprising vacuum glazing. In this paper, by using the paste of the glass frit base, the screen printing was performed. We analyzed the effect for the printing process to deposit pillar paste on the screen printing parameters by the factorial experimental design. The polynomial predicting the volume of the printed supporting pillars was drawn by using screen printing.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.694-697
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• 2005

The design of experiments are used for optimal design of formed tools to machine automobile bearing rubber seal die, which is classified into the high precision rubber mold. The clearance angle, rake angle and the length cutting edge are considered as the factors. The cutting force is selected to be a characteristic value and compared with the mean tool wear and life by repeated experiments. The design of the experiment is based on the repeated one-way factorial design, which finds the significance of the factors and the best level to predict the tool life by using ANOVA and regression.