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A comparison of designs for one‐step screening and response surface estimation
Author(s) -
Edwards David J.,
Truong David H. Q.
Publication year - 2011
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1191
Subject(s) - design of experiments , aliasing , construct (python library) , computer science , response surface methodology , process (computing) , surface (topology) , reliability engineering , industrial engineering , mathematical optimization , machine learning , engineering , artificial intelligence , mathematics , statistics , geometry , undersampling , programming language , operating system
Abstract The sequential design approach to response surface exploration is often viewed as advantageous as it provides the opportunity to learn from each successive experiment with the ultimate goal of determining optimum operating conditions for the system or process under study. Recent literature has explored factor screening and response surface optimization using only one three‐level design to handle situations where conducting multiple experiments is prohibitive. The most straightforward and accessible analysis strategy for such designs is to first perform a main‐effects only analysis to screen important factors before projecting the design onto these factors to conduct response surface exploration. This article proposes the use of optimal designs with minimal aliasing (MA designs) and demonstrates that they are more effective at screening important factors than the existing designs recommended for single‐design response surface exploration. For comparison purposes, we construct 27‐run MA designs with up to 13 factors and demonstrate their utility using established design criterion and a simulation study. Copyright 2011 © John Wiley & Sons, Ltd.