Journal Article10.2307/2348709
Designing Two‐Level Factorial Experiments Using Orthogonal Arrays When the Run Order is Important
P. C. Wang,H. W. Jan +1 more
37
TL;DR: In this paper, some useful properties of columns in the arrays are reviewed and several rules for the assignments are presented, helpful in designing experiments using orthogonal arrays.
read more
Abstract: Often industrial experiments require good fractional factorial designs to examine the effects of many factors by using only a small number of experimental runs. These experimental runs can be determined by assigning factors to the columns of appropriate orthogonal arrays. When the experimental runs are carried out in a time order sequence, the responses can depend on the run order. Frequently level changes are more expensive for some factors in the study than for others. To avoid unwanted time effects and to reduce costs, information is needed about the columns of the orthogonal arrays to assign factors to appropriate columns. In this paper, we review some useful properties of columns in the arrays and present several rules for the assignments. These are helpful in designing experiments using orthogonal arrays. For illustration, several examples are given after these rules have been presented.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Book
A Comprehensive Guide to Factorial Two-Level Experimentation
Robert W. Mee
- 10 Aug 2009
TL;DR: Fractional Factorial Design Examples: The basics of fractional factorial designs are discussed in detail in this article, where the authors present an analysis of full-factorial experiments with two-level factors.
Experimentation order with good properties for 2 k factorial designs
TL;DR: In this paper, the authors proposed a new methodology to obtain experimentation orders with the desired properties for designs with any number of runs, and showed that an important proportion of random orders achieve the same degree of protection as that obtained by experimenting in the design matrix standard order.
34
Experimentation order in factorial designs: new findings
TL;DR: This paper provides the best possible sequences for designs with 32 experiments, as well as sequences that offer excellent properties for Designs with 64 and 128 experiments.
26
•Journal Article
Designing two-level fractional factorial experiments in blocks of size two
TL;DR: Several assignment rules are suggested for designing good experiments to deal with the trade-off between run-size reduction and the possibly negligible effects in two-level factorial experiments.
18
References
•Book
Planning of Experiments
David Cox
- 01 Jan 1958
TL;DR: Subjects include the justification and practical difficulties of randomization, various factors occurring in factorial experiments, selecting the size of an experiments, different purposes for which observations may be made and much more.
1K
•Book
Introduction to Quality Engineering: Designing Quality into Products and Processes
Genichi Taguchi
- 01 Oct 1986
TL;DR: In this paper, a case study of variance loss and tolerance design for online and offline quality control is presented, where the S/N ratio is bypassed by a spring experiment.
1K
Minimum Cost Trend-Free Run Orders of Fractional Factorial Designs
TL;DR: In this article, run orders of fractional factorial designs which minimize a cost function based on the number of times the factors change levels during the time sequence in which the runs are performed and which simultaneously have all factor main effects components orthogonal to a polynomial time trend are found for a wide variety of factorial plans.
Interaction Graphs: Graphical Aids for Planning Experiments
Raghu N. Kacker,Kwok-Leung Tsui +1 more
TL;DR: Interaction graphs are graphical aids to plan fractional factorial experiments as discussed by the authors, which can be used to generate a plan from an orthogonal array by selecting certain columns of the orthogonality and deleting the rest.
52
Some Run Orders Requiring a Minimum Number of Factor Level Changes for the 24 and 25 Main Effect Plans
TL;DR: This paper describes a study which utilized a computer program written to examine run orders requiring a minimum number of factor level changes and select those in which the correlations between the main effects and a linear time trend are small.
52