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Comparing methods for design follow‐up: revisiting a metal‐cutting case study
Author(s) -
Edwards David J.,
Weese Maria L.,
Palmer Gregory A.
Publication year - 2013
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.1988
Subject(s) - fractional factorial design , plackett–burman design , context (archaeology) , design of experiments , fraction (chemistry) , factorial experiment , selection (genetic algorithm) , computer science , mathematical optimization , aliasing , optimal design , mathematics , econometrics , statistics , machine learning , filter (signal processing) , response surface methodology , paleontology , chemistry , organic chemistry , biology , computer vision
Adding another fraction to an initial fractional factorial design is often required to resolve ambiguities with respect to aliasing of factorial effects from the initial experiment and/or to improve estimation precision. Multiple techniques for design follow‐up exist; the choice of which is often made on the basis of the initial design and its analysis, resources available, experimental objectives, and so on. In this paper, we compare four design follow‐up strategies: foldover, semifoldover, D ‐optimal, and Bayesian ( MD ‐optimal) in the context of a metal‐cutting case study previously utilized to compare fractional factorials of different run sizes. Follow‐up designs are compared for each of a2 III 6 − 3 ,2 IV 6 − 2 , and Plackett–Burman initial experiments. Our empirical results suggest that a single follow‐up strategy does not outperform all others in every situation. This case study serves to illustrate design augmentation possibilities for practitioners and provides some basis for the selection of a follow‐up experiment. Copyright © 2013 John Wiley & Sons, Ltd.