Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Anàlisi de la capacitat del procés multiresposta× | Disseny d'experiments multiresposta× | |
|---|---|---|
| Camp | Disseny experimental | Disseny experimental |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | 1993–1994 (foundational multivariate indices) | 1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s |
| Autor original≠ | Taam, Subbaiah & Liddy (multivariate capability); Hubele, Shahriari & Cheng (MCpm) | Derringer & Suich (desirability function); Montgomery (systematic DoE integration) |
| Tipus≠ | Quantitative quality / process assessment method | Experimental optimization methodology |
| Font seminal≠ | Taam, W., Subbaiah, P., & Liddy, J. W. (1993). A note on multivariate capability indices. Journal of Applied Statistics, 20(3), 339–351. link ↗ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| Àlies | MRPCA, multivariate process capability, multi-characteristic capability analysis, vector process capability | Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE |
| Relacionats≠ | 6 | 4 |
| Resum≠ | Multi-response process capability analysis extends classical single-response capability indices (Cp, Cpk) to situations where a process must simultaneously satisfy specification limits on two or more correlated quality characteristics. Rather than evaluating each response in isolation, it assesses the joint probability that all characteristics fall within their respective tolerance regions, yielding a more realistic picture of overall process performance in multi-characteristic manufacturing and engineering settings. | Multi-response Design of Experiments (MRDoE) extends classical DoE to situations where several response variables must be optimized simultaneously. Rather than tuning factors for a single output, the experimenter fits separate regression or response-surface models for each response, then combines them — most often via Derringer and Suich's desirability function — into a single composite score that guides the search for factor settings satisfying all response targets at once. |
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