Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Gevoeligheidsanalyse-geïntegreerde Response Surface Methodology× | Response Surface Methodology (RSM)× | |
|---|---|---|
| Vakgebied | Experimenteel ontwerp | Experimenteel ontwerp |
| Familie≠ | Process / pipeline | Hypothesis test |
| Jaar van ontstaan≠ | 1990s–2000s (integration practice) | 1951 |
| Grondlegger≠ | Box & Wilson (RSM, 1951); Saltelli et al. (global SA framework, 1990s–2000s) | George E. P. Box & K. B. Wilson |
| Type≠ | Hybrid experimental-analytical method | Second-order polynomial response surface model |
| Oorspronkelijke bron≠ | 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: 978-1118916018 | 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. link ↗ |
| Aliassen≠ | SA-RSM, RSM with sensitivity analysis, sensitivity-augmented RSM, response surface methodology with factor screening | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Verwant≠ | 5 | 7 |
| Samenvatting≠ | Sensitivity analysis-integrated RSM couples a structured experimental design with a formal sensitivity analysis of the fitted response surface model. After estimating a polynomial surrogate from designed experiments, global or local sensitivity indices are computed to quantify each input factor's relative contribution to output variability. This allows practitioners to identify which factors truly drive the response before committing to full optimization, reducing cost and improving the reliability of the final optimum. | Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics. |
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