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| Többválaszos Statisztikai Folyamatszabályozás – Multivariáns SPC× | Többváltozós Változási Felület Módszertan× | |
|---|---|---|
| Tudományterület | Kísérlettervezés | Kísérlettervezés |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1947 (Hotelling's T²); mature multivariate SPC framework 1980s–2000s | 1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson) |
| Megalkotó≠ | Harold Hotelling (T² statistic); extended by Alt, Lowry, Montgomery, Mason & Young | Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework) |
| Típus≠ | Multivariate quality-monitoring procedure | Experimental optimization technique |
| Alapmű≠ | Lowry, C. A., & Montgomery, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800–810. DOI ↗ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| Alternatív nevek | Multivariate SPC, MSPC, Multi-response SPC, Multivariate statistical process control | Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization |
| Kapcsolódó | 6 | 6 |
| Összefoglaló≠ | Multi-response statistical process control (multivariate SPC) extends classical univariate control charting to processes where two or more correlated quality characteristics must be monitored simultaneously. By treating all responses as a joint distribution, it detects shifts that would be invisible when each response is charted independently, reducing false alarms and improving the sensitivity of process monitoring in manufacturing and service contexts. | 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. |
| ScholarGateAdatkészlet ↗ |
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