विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| जोखिम-आधारित केंद्रीय संयुक्त डिज़ाइन× | बॉक्स-बेहंकेन 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 | 1960 |
| प्रवर्तक≠ | Foundational CCD: George E. P. Box & K. B. Wilson (1951); risk integration adapted from engineering risk analysis traditions | George E. P. Box and Donald W. Behnken |
| प्रकार≠ | Experimental design with integrated risk assessment | Response surface design (incomplete three-level factorial) |
| 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. DOI ↗ | Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475. DOI ↗ |
| उपनाम | Risk-informed CCD, CCD with risk assessment, Uncertainty-aware central composite design, Risk-integrated RSM | BBD, Box-Behnken, Box-Behnken RSM design, three-level incomplete factorial 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. | The Box-Behnken design (BBD) is an efficient response surface methodology design that fits a full second-order polynomial model using three levels of each factor. Introduced by Box and Behnken in 1960, it places experimental points at the midpoints of the edges of a hypercube and at the center, avoiding the corner points where all factors are simultaneously at their extreme levels. This structure makes BBD particularly attractive when extreme-level combinations are physically impossible, costly, or unsafe to test. |
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