เปรียบเทียบวิธี

	ระเบียบวิธีพื้นผิวการตอบสนองตามความเสี่ยง	Central Composite Design
สาขาวิชา	การออกแบบการทดลอง	การออกแบบการทดลอง
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1990s–2000s (risk-based extensions)	1951
ผู้ริเริ่ม	Builds on Box & Wilson (1951) RSM; risk integration formalized in engineering reliability literature from the 1990s onward	George E. P. Box and K. B. Wilson
ประเภท	Experimental optimization with probabilistic risk constraints	Response surface experimental design
แหล่งต้นตำรับ	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	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.
ชื่อเรียกอื่น	Risk-based RSM, reliability-based RSM, probabilistic RSM, risk-integrated response surface methodology	CCD, Box-Wilson design, central composite response surface design, rotatable central composite design
ที่เกี่ยวข้อง	5	3
สรุป	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.	Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing.