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2019 | OriginalPaper | Chapter

13. DOE: Response Surface Methods

Author : Theodore T. Allen

Published in: Introduction to Engineering Statistics and Lean Six Sigma

Publisher: Springer London

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Abstract

Response surface methods (RSM) are primarily relevant when the decision-maker desires (1) to create a relatively accurate prediction of engineered system input-output relationships and (2) to “tune” or optimize thoroughly of the system being designed. Since these methods require more runs for a given number of factors than screening using fractional factorials, they are generally reserved for cases in which the importance of all factors is assumed, perhaps because of previous experimentation.

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Literature
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go back to reference Box GEP, Draper NR (1987) Empirical model-building and response surfaces. Wiley, New YorkMATH Box GEP, Draper NR (1987) Empirical model-building and response surfaces. Wiley, New YorkMATH
go back to reference Box GEP, Wilson KB (1951) On the experimental attainment of optimum conditions. J Roy Stat Soc B 13:1–45MathSciNetMATH Box GEP, Wilson KB (1951) On the experimental attainment of optimum conditions. J Roy Stat Soc B 13:1–45MathSciNetMATH
go back to reference Choudhury AK (1997) Study of the effect of die casting machine upon die deflections. Master’s thesis, Industrial & Systems Engineering, The Ohio State University, Columbus Choudhury AK (1997) Study of the effect of die casting machine upon die deflections. Master’s thesis, Industrial & Systems Engineering, The Ohio State University, Columbus
go back to reference Myers R, Montgomery D (2001) Response surface methodology, 5th edn. John Wiley & Sons Inc, Hoboken, NJ Myers R, Montgomery D (2001) Response surface methodology, 5th edn. John Wiley & Sons Inc, Hoboken, NJ
Metadata
Title
DOE: Response Surface Methods
Author
Theodore T. Allen
Copyright Year
2019
Publisher
Springer London
DOI
https://doi.org/10.1007/978-1-4471-7420-2_13

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