Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Multi-respons Full Factorial Design× | Metodikk for responsoverflate med flere responser× | |
|---|---|---|
| Fagfelt | Forsøksdesign | Forsøksdesign |
| Familie | Process / pipeline | Process / pipeline |
| Opprinnelsesår≠ | 1950s–1980s | 1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson) |
| Opphavsperson≠ | Douglas C. Montgomery (factorial framework); Derringer & Suich (multi-response desirability optimization) | Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework) |
| Type≠ | Experimental design with multi-objective optimization | Experimental optimization technique |
| Opprinnelig kilde≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443 | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| Alias | MRFFD, multi-response FFD, multiple-response full factorial, multi-objective full factorial design | Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization |
| Relaterte≠ | 3 | 6 |
| Sammendrag≠ | Multi-response full factorial design extends the classic full factorial experiment by measuring and jointly optimizing two or more response variables at the same time. Every combination of all factor levels is tested, providing complete main-effect and interaction information for each response. A desirability function or Pareto-front approach then reconciles competing responses into a single optimal factor setting, making this the method of choice when engineering or process goals involve trade-offs among several quality characteristics simultaneously. | 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. |
| ScholarGateDatasett ↗ |
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