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Design‐of‐Experiments Methods and their Application to Robust Multicriteria Optimization Problems
Author(s) -
Lautenschlager U.,
Eschenauer H. A.,
Mistree F.
Publication year - 1999
Publication title -
zamm ‐ journal of applied mathematics and mechanics / zeitschrift für angewandte mathematik und mechanik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.449
H-Index - 51
eISSN - 1521-4001
pISSN - 0044-2267
DOI - 10.1002/zamm.19990791319
Subject(s) - taguchi methods , design of experiments , mathematical optimization , computer science , series (stratigraphy) , multi objective optimization , orthogonal array , space (punctuation) , surface (topology) , response surface methodology , mathematics , machine learning , paleontology , statistics , geometry , biology , operating system
Design‐of‐experiments (DOE) methods are utilized to explore the design space and to build response surface models in order to facilitate the effective solution of multiobjective optimization problems. Response surface models provide an efficient means to rapidly model the trade‐off between conflicting goals. For robust design applications these approximation models are used efficiently and effectively to calculate variances due to noise factors using Taylor series expansions. This combined‐army approach is compared to Taguchi's original crossed‐array approach. The shape optimization of a flywheel with two conflicting design goals is used to illustrate the approach.