algo-mfg-doelisted

# Design of Experiments (DOE) ## Overview DOE systematically varies process factors to identify their effects on responses. Full factorial tests all combinations; fractional factorial tests a strategic subset. Identifies main effects and interactions. More efficient than one-factor-at-a-time (OFAT) which misses interactions. Uses ANOVA for analysis. ## When to Use **Trigger conditions:** - Identifying which process factors significantly affect quality/yield - Optimizing process settings for target performance - Screening many factors to find the vital few **When NOT to use:** - When the process is not stable (stabilize with SPC first) - For observational data with no ability to manipulate factors ## Algorithm ``` IRON LAW: One-Factor-At-A-Time (OFAT) MISSES Interactions Changing one factor while holding others fixed cannot detect interactions (where the effect of A depends on the level of B). Full factorial or fractional factorial designs test ALL main effects AND interactions in fewer runs than OFAT. A 2³ factorial (8 runs) gives more information than 6 OFAT runs at lower cost. ``` ### Phase 1: Input Validation Define: response variable(s), factors (2-7 practical), levels per factor (usually 2 for screening, 3 for optimization), constraints, noise factors. **Gate:** Factors and levels defined, practical to run all experimental conditions. ### Phase 2: Core Algorithm **Screening (many factors):** 2^(k-p) fractional factorial. Choose resolution III+ (main effects not