Comparar métodos

	Metodologia de Superfície de Resposta Baseada em Risco	Metodologia Robusta de Superfície de Resposta
Área	Delineamento experimental	Delineamento experimental
Família	Process / pipeline	Process / pipeline
Ano de origem	1990s–2000s (risk-based extensions)	1990
Autor original	Builds on Box & Wilson (1951) RSM; risk integration formalized in engineering reliability literature from the 1990s onward	G. G. Vining and Raymond H. Myers (dual response formulation)
Tipo	Experimental optimization with probabilistic risk constraints	Experimental optimization technique
Fonte seminal	Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley. ISBN: 978-0470174463	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.
Outros nomes	Risk-based RSM, reliability-based RSM, probabilistic RSM, risk-integrated response surface methodology	Robust RSM, dual response surface methodology, robust parameter design via RSM, mean-variance RSM
Relacionados	5	3
Resumo	Risk-based Response Surface Methodology (Risk-based RSM) extends classical RSM by embedding probabilistic risk or reliability constraints into the experimental optimization process. Rather than seeking a single optimal point under deterministic conditions, it identifies factor settings that achieve performance goals while keeping the probability of failure or unacceptable outcomes below a specified threshold — making it especially valuable in safety-critical and high-variability engineering contexts.	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.