Võrdle meetodeid
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| Simulation-assisted full factorial design× | Simulatsioonipõhine vastuspinnametoodika× | |
|---|---|---|
| Valdkond | Katsedisain | Katsedisain |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 1990s–2000s (simulation-DOE integration formalized) | 1951 (RSM); simulation integration widely adopted from 1980s onward |
| Looja≠ | Montgomery (DOE foundations); Kleijnen (simulation DOE formalization) | Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions |
| Tüüp≠ | Experimental design with computer simulation | Experimental optimization method |
| Algallikas≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478 | 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 |
| Rööpnimetused | SA-FFD, computer simulation full factorial, virtual full factorial design, simulation-based full factorial DOE | SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM |
| Seotud≠ | 4 | 6 |
| Kokkuvõte≠ | Simulation-assisted full factorial design integrates full factorial design of experiments (DOE) with computer simulation models — such as discrete-event simulation, finite element analysis, or Monte Carlo methods — to systematically explore every combination of factor levels and quantify their effects on system responses. It enables comprehensive experimentation in contexts where physical trials would be costly, dangerous, or infeasible. | 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. |
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