Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Jutīguma analīzē integrēta atbildes virsmas metodoloģija× | Centrālais kompozītais plāns× | |
|---|---|---|
| Nozare | Eksperimentu plānošana | Eksperimentu plānošana |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1990s–2000s (integration practice) | 1951 |
| Autors≠ | Box & Wilson (RSM, 1951); Saltelli et al. (global SA framework, 1990s–2000s) | George E. P. Box and K. B. Wilson |
| Tips≠ | Hybrid experimental-analytical method | Response surface experimental design |
| Pirmavots≠ | 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. DOI ↗ |
| Citi nosaukumi | SA-RSM, RSM with sensitivity analysis, sensitivity-augmented RSM, response surface methodology with factor screening | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | 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. | 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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