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| Wielowyjściowy ułamkowy plan czynnikowy× | Metodologia Wielowykresowych Powierzchni Odpowiedzi× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1961 (fractional factorial foundation); 1980 (multi-response desirability approach) | 1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson) |
| Twórca≠ | George E.P. Box, J. Stuart Hunter, and William G. Hunter (fractional factorial basis); Derringer & Suich (multi-response desirability extension) | Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework) |
| Typ≠ | Experimental design with simultaneous multi-response optimization | Experimental optimization technique |
| Źródło pierwotne | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| Inne nazwy | MRFFD, multi-response FFD, multi-objective fractional factorial design, simultaneous multi-response fractional factorial | Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization |
| Pokrewne≠ | 4 | 6 |
| Podsumowanie≠ | Multi-response fractional factorial design (MRFFD) applies a resolution-efficient fractional factorial experiment to study multiple response variables simultaneously. By running only a carefully chosen fraction of the full factorial treatment combinations, the experimenter gathers enough information to fit individual response models for each output and then optimize all responses jointly — typically via a composite desirability function — while keeping the number of experimental runs tractable. | Multi-response Response Surface Methodology (MRSM) extends classical RSM to situations where an experiment generates two or more response variables that must be optimized simultaneously. Rather than tuning factor settings for a single output, MRSM fits a separate second-order polynomial model for each response, then combines them — most commonly via Derringer and Suich's desirability function — to find factor settings that satisfy all objectives at once. |
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