• Title/Summary/Keyword: Partially Balanced Array

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Study on the Optimality of 2-level Resolution V Minimal Fractional Factorial Designs (2-수준계 Resolution V 최소 부분실험법의 최적성에 관한 연구)

  • Kim Sang Ik
    • Journal of Korean Society for Quality Management
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    • v.32 no.3
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    • pp.234-243
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    • 2004
  • In this paper, we study the optimality of 2-level resolution V minimal fractional factorial designs which can be constructed by using a partially balanced array. Moreover the relative efficiencies of such designs are compared in the sense of three optimality criteria such as determinant(D)-optimality, trace(A)-optimality, and eigenvalue(E) -optimality criterion.

A Study on the Construction and Analysis of Fractional Designs by Using Arrays for Factorial Experiments (배열을 이용한 효과적인 일부실시법의 설계 및 분석방법에 관한 연구)

  • Kim, Sang-Ik
    • Journal of Korean Society for Quality Management
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    • v.40 no.1
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    • pp.15-24
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    • 2012
  • For the construction of fractional factorial designs, the various arrays can be widely used. In this paper we review the statistical properties of fractional designs constructed by two arrays such as orthogonal array and partially balanced array, and develop a quick and easy method for analyzing unreplicated saturated designs. The proposed method can be characterized that we control the error rate by experiment-wise way and exploit the multivariate Student $t$-distribution. Especially the proposed method can be used efficiently together with some exploratory analysis methods, such as half normal probability plot method.

A Study on the Determination of Experimental Size of Near-orthogonal Two-level Balanced Trace Optimal Resolution-V Fractional Factorial Designs (직교성에 가까운 트레이스 최적 2-수준 Resolution-V 균형 일부실험법의 실험크기 결정에 관한 연구)

  • Kim, Sang Ik
    • 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.

$ fractional factorial designs of resolution V and taguchi method

  • 김상익
    • The Korean Journal of Applied Statistics
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    • v.5 no.1
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    • pp.19-28
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    • 1992
  • In this paper, minimal balanced $2^t$ fractional factorial designs which permit the estimation of main effects and 2-factor interactions are developed by using a partially balanced array. Such designs are characterized by a minimum number of runs and some balancedness property of the variance-covariance matrix of the estimates. In addition to describing the designs, optimality criteria are discussed and the trace-optimal designs are presented. The proposed designs are especially useful in Taguchi method, where we need to investigate up to 2-factor interactions of the control factors.

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