Year: 2015
Type: article
Abstract: Personal empirical experience when lecturing and consulting shows that not only students, but also experienced engineers familiar with DOE, show much more interest in the modeling of a process than to statistical inference, neglecting attention to “boundary conditions” of the process. But exactly the observation of ancillary boundary conditions of experiments, such as minimizing Beta-risk and noise, is determinant for the efficient execution of an experimental design and the effective application of DOE derived models. This essay focuses attention to the must-dos in the DOE statistics approach in order to avoid research pitfalls by presenting a fail-proof 14-step approach when applying DOE modeling.
Authors: Bruno G. Rüttimann, Konrad Wegener
Institutions: ETH Zurich, Inspire, Institute of Machine Tools and Manufacturing
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W2083370140