পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| Multi-Response Full Factorial Design× | মাল্টি-রেসপন্স রেসপন্স সারফেস মেথডোলজি× | |
|---|---|---|
| ক্ষেত্র | পরীক্ষামূলক নকশা | পরীক্ষামূলক নকশা |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1950s–1980s | 1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson) |
| প্রবর্তক≠ | Douglas C. Montgomery (factorial framework); Derringer & Suich (multi-response desirability optimization) | Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework) |
| ধরন≠ | Experimental design with multi-objective optimization | Experimental optimization technique |
| মৌলিক উৎস≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443 | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| অপর নাম | MRFFD, multi-response FFD, multiple-response full factorial, multi-objective full factorial design | Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization |
| সম্পর্কিত≠ | 3 | 6 |
| সারসংক্ষেপ≠ | Multi-response full factorial design extends the classic full factorial experiment by measuring and jointly optimizing two or more response variables at the same time. Every combination of all factor levels is tested, providing complete main-effect and interaction information for each response. A desirability function or Pareto-front approach then reconciles competing responses into a single optimal factor setting, making this the method of choice when engineering or process goals involve trade-offs among several quality characteristics simultaneously. | Multi-response Response Surface Methodology (MRSM) extends classical RSM to situations where an experiment generates two or more response variables that must be optimized simultaneously. Rather than tuning factor settings for a single output, MRSM fits a separate second-order polynomial model for each response, then combines them — most commonly via Derringer and Suich's desirability function — to find factor settings that satisfy all objectives at once. |
| ScholarGateডেটাসেট ↗ |
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