Abstract
In process robustness studies, it is desirable to simultaneously minimize the influence of noise factors on the system and to determine the levels of controllable factors that will optimize the overall response or outcome. A methodology for evaluating designed experiments that involve both controllable and uncontrollable, or noise, factors is outlined and presented in this paper. Two variance expressions are developed for evaluating competing experimental design strategies. The maximum, average, and minimum scaled prediction error variances resulting from the models developed are displayed visually on variance dispersion graphs. The scaled prediction error variances account for mean model errors as well as variation transmitted to the process by noise variables.
Original language | English (US) |
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Pages (from-to) | 54-70 |
Number of pages | 17 |
Journal | Journal of Quality Technology |
Volume | 34 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2002 |
Keywords
- Central composite designs
- Design of experiments
- Response surface methodology
- Robust parameter design
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering