Design of Experiment (DOE)
Full Factorial Data Analysis
› Determine the effect of single factors and their ranking.
Figure 1: Graph of Full Factorial Data
Legend:
Factor A: Arm Length.
This affects the amount of energy that is provided to the projectile where shorter arm length provides more energy for the projectile to go further.
Factor B: Projectile weight.
The weight of the projectile determines the amount of energy that is required for the projectile to fly. A lower amount of weight would mean a lower amount of energy is needed for the projectile to go further.
Factor C: Stop angle.
The angle at which the arm is launched at will affect the amount of energy that is provided to the projectile. A smaller stop angle from the ground will provide more energy for the projectile to go further.
Ranking:
1. Factor C
2. Factor A
3. Factor B
› Determine the interaction effects
Figure 2: Interaction effect of A x B
Both lines have a significant positive gradient and are at different values. Hence, there is a significant effect between factors A and B.
Figure 3: Interaction effect of A x C
Both lines have a very minor positive gradient. Since the lines are parallel to each other, there is little to no interaction between factors A and C.
Figure 4: Interaction effect of B x C
› Include all tables and graphs both as pictures and as excel file (hyperlink to google
drive or OneDrive)
https://docs.google.com/spreadsheets/d/1wNJ5v8UNIKZo-YKT8vi4bKA8UL0jzNVd/edit?usp=sharing&ouid=105556959392100484114&rtpof=true&sd=true
› Include the conclusion of the data analysis for full factorial data analysis
To obtain the furthest distance,
> shorter arm length, lower projectile weight and lower stop angle should be used.
Factor C has the most significant effect as it has the highest gradient out of all the factors, followed by factor A and B.
Fractional Data Analysis
› Determine the effect of single factors and their ranking.
Figure 5: Graph of Fractional Factorial Data
Legend:
Factor A: Arm Length.
This affects the amount of energy that is provided to the projectile where shorter arm length provides more energy for the projectile to go further.
Factor B: Projectile weight.
The weight of the projectile determines the amount of energy that is required for the projectile to fly. A lower amount of weight would mean a lower amount of energy is needed for the projectile to go further.
Factor C: Stop angle.
The angle at which the arm is launched at will affect the amount of energy that is provided to the projectile. A smaller stop angle from the ground will provide more energy for the projectile to go further.
Ranking:
1. Factor C
2. Factor B
3. Factor A
› Include all tables and graphs both as pictures and as excel file (hyperlink to google
drive or OneDrive)
https://docs.google.com/spreadsheets/d/1wNJ5v8UNIKZo-YKT8vi4bKA8UL0jzNVd/edit?usp=sharing&ouid=105556959392100484114&rtpof=true&sd=true
› Include the conclusion of the data analysis for fractional factorial data analysis.
To obtain the furthest distance,
> shorter arm length, lower projectile weight and lower stop angle should be used.
Factor C has the most significant effect as it has the highest gradient out of all the factors, followed by B and then A.
› Personal Reflection
From the tutorial, I learnt that full fractional data analysis is much more accurate and efficient as compared to fractional data analysis since it takes all the runs and data into consideration however, fractional data analysis is still useful incase there is insufficient time.
The experiment itself was very engaging even though we encountered a number of challenges such as the tension of our catapult not being enough to fly the projectile further than 100cm. It was a little frustrating seeing as our group didn't manage to hit down all the targets during the group challenge despite all the efforts we put in but it was still a worthwhile experience in the end since there were a lot of memories made. Overall, it was interesting being able to apply what we learnt in class directly into our practical.
Case Study
What could be simpler than making microwave popcorn? Unfortunately, as everyone who has ever
made popcorn knows, it’s nearly impossible to get every kernel of corn to pop. Often a considerable
number of inedible “bullets” (un-popped kernels) remain at the bottom of the bag. What causes this
loss of popcorn yield? In this case study, three factors were identified:
1. Diameter of bowls to contain the corn, 10 cm and 15 cm
2. Microwaving time, 4 minutes and 6 minutes
3. Power setting of microwave, 75% and 100%
8 runs were performed with 100 grams of corn used in every experiments and the measured
variable is the amount of “bullets” formed in grams and data collected are shown below:
Factor A= diameter
Factor B= microwaving time
Factor C= power
Full Factorial
Figure 6: Graph of Full Factorial Data for Case Study
Ranking:
1. Factor C
2. Factor B
3. Factor A
From the graph, Factor C (power) is the most influential factor as it has the largest gradient, followed by B and then A.
Fractional Factorial
Figure 7: Graph for Fractional Factorial Data for Case Study
Ranking:
1. Factor C
2. Factor B
3. Factor A
From the graph, Factor C (power) is the most influential factor as it has the largest gradient, followed by B and then A.
Comments
Post a Comment