Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Disseny d'experiments multiresposta× | Dissenys Composats Centrals× | |
|---|---|---|
| Camp | Disseny experimental | Disseny experimental |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | 1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s | 1951 |
| Autor original≠ | Derringer & Suich (desirability function); Montgomery (systematic DoE integration) | George E. P. Box and K. B. Wilson |
| Tipus≠ | Experimental optimization methodology | Response surface experimental design |
| Font seminal≠ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ | 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 ↗ |
| Àlies | Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Relacionats≠ | 4 | 3 |
| Resum≠ | Multi-response Design of Experiments (MRDoE) extends classical DoE to situations where several response variables must be optimized simultaneously. Rather than tuning factors for a single output, the experimenter fits separate regression or response-surface models for each response, then combines them — most often via Derringer and Suich's desirability function — into a single composite score that guides the search for factor settings satisfying all response targets at once. | 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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