Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Simulatie-ondersteunde respons-oppervlaktemethodologie× | Robuuste Respons Surface Methodologie× | |
|---|---|---|
| Vakgebied | Experimenteel ontwerp | Experimenteel ontwerp |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1951 (RSM); simulation integration widely adopted from 1980s onward | 1990 |
| Grondlegger≠ | Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions | G. G. Vining and Raymond H. Myers (dual response formulation) |
| Type≠ | Experimental optimization method | Experimental optimization technique |
| 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: 978-1118916025 | Vining, G. G., & Myers, R. H. (1990). Combining Taguchi and response surface philosophies: A dual response approach. Journal of Quality Technology, 22(1), 38–45. DOI ↗ |
| Aliassen | SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM | Robust RSM, dual response surface methodology, robust parameter design via RSM, mean-variance RSM |
| Verwant≠ | 6 | 3 |
| Samenvatting≠ | Simulation-assisted response surface methodology (SA-RSM) combines computer simulation models — such as finite element analysis, computational fluid dynamics, or discrete-event simulation — with the statistical framework of response surface methodology to efficiently map, model, and optimize system responses. Instead of running physical experiments, the researcher executes simulation runs at design points prescribed by an RSM design, fits a polynomial metamodel (surrogate) to the simulation outputs, and uses that metamodel to locate optimal factor settings. | Robust Response Surface Methodology (Robust RSM) is an experimental optimization strategy that simultaneously fits two regression models — one for the mean response and one for its variance (or standard deviation) — across a designed experiment. By jointly optimizing these dual surfaces, engineers identify factor settings that hit a performance target while minimizing process variability, combining the empirical model-building power of classical RSM with the variance-reduction goals of robust parameter design. |
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