DESIGN Of EXPERIMENT

Role: Chief Safety Officer

Excel Hyperlink: https://docs.google.com/spreadsheets/d/17aIHWlhpzsuNw2YPzGiCF6FGAxYI4TZY/edit?usp=sharing&ouid=114481911011011528964&rtpof=true&sd=true

Fractional Factorial Data Analysis

Runs Chosen: 1,4,6,7

Effect of Single Factors and their Rankings

From the graph, we observe factors B (treatment temperature) and C (stirring speed) to be of equal or close significance while factor A (concentration of coagulant) is the least significant judging by the steepness of each lines.

Interaction Effects

A x B

At LOW B, Average of low A = 5

At LOW B, Average of high A = 3

At LOW B, Total Effect of A = 3 -5 = -2 (decrease)

At HIGH B, Average of low A = 5

At HIGH B, Average of high A = 33

At HIGH B, Total Effect of A =  33 - 5 = 28 (increase)

B x C

At LOW C, Average of low B = 5

At LOW C, Average of high B = 33 

At LOW C, Total Effect of B = 33 - 5 = 28 (increase)

At HIGH C, Average of low B = 3

At HIGH C, Average of high B = 5

At HIGH C, Total Effect of B = 5 - 3 = 2 (increase)

A x C

At LOW C, Average of low A = 5

At LOW C, Average of high A = 33

At LOW C, Total Effect of A = 33 - 5 = 28 (increase)

At HIGH C, Average of low A = 5

At HIGH C, Average of high A = 3 

At HIGH C, Total Effect of A = 3 - 5 = -2 (decrease)

Conclusion of the Data Analysis

Order of Significance (Highest to Lowest): B, C, A

All combinations have the same interaction effect.

Reflection Journal

From this particular topic, I learnt how to perform experiments to a much significant efficiency. My CPDD team will definitely benefit a lot from this way of experiment, where we can cut down our experiments by a huge margin. We can also figure out what controllable factors are affecting our prototype the most and also how to efficiently bring out the best possible results with lower amount of experiments.