विधियों की तुलना करें

	जोखिम-आधारित केंद्रीय संयुक्त डिज़ाइन	सेंट्रल कंपोजिट डिज़ाइन
क्षेत्र	प्रयोगात्मक अभिकल्प	प्रयोगात्मक अभिकल्प
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1951 (CCD); risk-based integration emerged in applied engineering literature from the 1990s onward	1951
प्रवर्तक	Foundational CCD: George E. P. Box & K. B. Wilson (1951); risk integration adapted from engineering risk analysis traditions	George E. P. Box and K. B. Wilson
प्रकार	Experimental design with integrated risk assessment	Response surface experimental design
मौलिक स्रोत	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.	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.
उपनाम	Risk-informed CCD, CCD with risk assessment, Uncertainty-aware central composite design, Risk-integrated RSM	CCD, Box-Wilson design, central composite response surface design, rotatable central composite design
संबंधित	5	3
सारांश	Risk-based Central Composite Design (Risk-based CCD) integrates formal risk identification and uncertainty quantification into the classical CCD framework. By coupling the rotatable second-order experimental structure of CCD with probabilistic risk metrics, engineers and scientists can simultaneously optimize process responses and characterize the risk of unacceptable outcomes — making it particularly valuable in regulated industries such as pharmaceuticals, chemical engineering, and advanced manufacturing.	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.