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| Metodologia powierzchni odpowiedzi wspomagana symulacją× | Central Composite Design× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1951 (RSM); simulation integration widely adopted from 1980s onward | 1951 |
| Twórca≠ | Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions | George E. P. Box and K. B. Wilson |
| Typ≠ | Experimental optimization method | Response surface experimental design |
| Źródło pierwotne≠ | Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916025 | Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45. DOI ↗ |
| Inne nazwy | SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Pokrewne≠ | 6 | 3 |
| Podsumowanie≠ | Simulation-assisted response surface methodology (SA-RSM) combines computer simulation models — such as finite element analysis, computational fluid dynamics, or discrete-event simulation — with the statistical framework of response surface methodology to efficiently map, model, and optimize system responses. Instead of running physical experiments, the researcher executes simulation runs at design points prescribed by an RSM design, fits a polynomial metamodel (surrogate) to the simulation outputs, and uses that metamodel to locate optimal factor settings. | Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing. |
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