เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การออกแบบเต็มรูปแบบตามความเสี่ยง× | การทดลองแบบแฟกทอเรียลเต็มรูปแบบที่ทนทาน× | |
|---|---|---|
| สาขาวิชา | การออกแบบการทดลอง | การออกแบบการทดลอง |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | 2000s (formal integration with risk frameworks circa 2005–2009) | 1980s–1990s |
| ผู้ริเริ่ม≠ | Developed at the intersection of classical factorial experimentation (Fisher, 1935) and formal risk analysis frameworks (ICH Q8/Q9, 2005–2009) | Genichi Taguchi (robustness principles); formalized in combined-array form by Shoemaker, Tsui, and Wu (1991) |
| ประเภท≠ | Structured experimental design with risk-informed factor prioritization | Experimental design with noise-factor control |
| แหล่งต้นตำรับ≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478 | Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall. ISBN: 978-0137451678 |
| ชื่อเรียกอื่น | risk-informed full factorial design, RB-FFD, risk-prioritized factorial experiment, risk-based FFD | robust 2^k design, full factorial robust parameter design, robust FFD, noise-factor full factorial |
| ที่เกี่ยวข้อง≠ | 3 | 2 |
| สรุป≠ | Risk-based full factorial design integrates formal risk analysis — typically Failure Mode and Effects Analysis (FMEA) or a comparable risk-ranking tool — with a full factorial experiment to ensure that factors posing the greatest quality or safety risk receive exhaustive experimental coverage. All combinations of selected factor levels are run, but the selection of which factors to include and the range of their levels is explicitly guided by prior risk scores rather than purely by engineering intuition or resource availability. | Robust full factorial design extends the classical full factorial experiment by explicitly including noise factors — uncontrollable variables that cause performance variation in real-world conditions. By crossing all control factor levels with all noise factor levels in a single combined array, engineers identify control factor settings that maximize mean performance while minimizing sensitivity to noise, yielding products and processes that perform consistently across operating environments. |
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