Design of Experiments (DOE) Helicopters Team Rubber Ducky Emily Kalberer, Daniel Chavez, Allison Raniere Purpose of Experiment The purpose of this project is to use the DOE procedure to: Write a prediction equation for the frequency using Coded Factors. Predict the height of random dimensions given. Understand how DOE is more efficient versus the One-Factor-at-a-time tactic. Factors Blade Length (L) Fold Length (F) Low Setting: 14 cm High Setting: 28 cm Low Setting: 1 cm High Setting: 2cm Our Designs Low-Low (1) Setting Low-High (b) Setting High-Low (a) SettingHigh-High (ab) Setting Models in action (1) Avg. Height: 1.62m(b) Avg. Height: 0.93m (a) Avg. Height: 1.24m(ab) Avg. Height: 1.46m Main Effects Plot L F 7cm 14cm 1cm2cm Interaction Plot 0.5m 1.0m 1.5m 2.0m 0.93m 1.24m 1.24m (-) 1.46m (+) Shows evidence of Interaction Main Effects and Interaction Effects A = ((a-(1))+(ab-b))/2L = (( )+( ))/2 = B = ((b-(1))+(ab-a))/2F = (( )+( ))/2 = AB = ((ab+(1))-(a+b))/2LF = (( )-( ))/2 = Prediction Equation Y = K 1 +K A X A +K B X B +K AB X A X B K 1 = ((1)+b+a+ab)/4K 1 = ( )/4 = K A = (-(1)-b+a+ab)/4K A = ( )/4 = K B = (-(1)+b-a+ab)/4K B = ( )/4 = K AB = ((1)-b-a+ab)/4K AB = ( )/4 = Y = X A X B X A X B Predicted Height L: 13cm X A = (2(13)-14-7)/14-7 = F: 1.7cm X B = (2(1.7)-2-1)/2-1 = 0.4 Y = 1.42m Actual Height: 1.68m Coding: X A = (2A-A + -A - )/A + -A - Percent Error (Measured-Predicted)/Measured x 100 = Percent Error ( )/1.68 x 100 = 15% error Factor that had the greatest effect on height: Length of blade (main effect of 0.075) Conclusion Prediction model was useful in predicting the outcome because we created a small percentage of error through the coded factors of the dimensions specifically given to us. We had several sources of error such as: using different blades, imperfect measurements/ cuts, and varying fold angles. DOE was a useful technique to create a model in this situation because its predicted value for the specified measurements were precise compared to the actual recorded values. Our group would change our rounding for both our predicted and measured values.