方法对比

	灵敏度分析-集成响应面方法	响应面方法 (RSM)
领域	实验设计	实验设计
方法族	Process / pipeline	Hypothesis test
起源年份	1990s–2000s (integration practice)	1951
提出者	Box & Wilson (RSM, 1951); Saltelli et al. (global SA framework, 1990s–2000s)	George E. P. Box & K. B. Wilson
类型	Hybrid experimental-analytical method	Second-order polynomial response surface model
开创性文献	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: 978-1118916018	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
别名	SA-RSM, RSM with sensitivity analysis, sensitivity-augmented RSM, response surface methodology with factor screening	RSM, Central Composite Design, Box-Behnken Design, CCD
相关	5	7
摘要	Sensitivity analysis-integrated RSM couples a structured experimental design with a formal sensitivity analysis of the fitted response surface model. After estimating a polynomial surrogate from designed experiments, global or local sensitivity indices are computed to quantify each input factor's relative contribution to output variability. This allows practitioners to identify which factors truly drive the response before committing to full optimization, reducing cost and improving the reliability of the final optimum.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.