Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Carta de Controle Multivariada× | Desenho de Experimentos Multi-Resposta× | |
|---|---|---|
| Área | Delineamento experimental | Delineamento experimental |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions) | 1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s |
| Autor original≠ | Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others | Derringer & Suich (desirability function); Montgomery (systematic DoE integration) |
| Tipo≠ | Multivariate statistical process monitoring | Experimental optimization methodology |
| Fonte seminal≠ | Hotelling, H. (1947). Multivariate quality control illustrated by the air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of Statistical Analysis (pp. 111–184). McGraw-Hill. link ↗ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| Outros nomes | multivariate control chart, multi-response SPC, MRCC, multiple-response monitoring chart | Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | A multi-response control chart simultaneously monitors two or more correlated quality characteristics on a single chart, preserving the correlation structure that univariate charts ignore. Built on Hotelling's T² statistic and its time-weighted extensions (MEWMA, MCUSUM), it detects process shifts that would be missed if each response were charted independently. It is the standard tool in manufacturing and service quality when product performance depends on multiple interrelated outputs. | 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. |
| ScholarGateConjunto de dados ↗ |
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