Hi, I am running a DOE for coating thickness and uniformity. I have 5 factors and am running a fractional design to reduce the number of trials to 16. My responses are all the same metric, which is thickness. But they are in different fixed locations (Z1,Z2..etc). In the end of this test i want to determine which factors have the largest effect on uniformity, determine any interactions and most importantly determine which factors give me the best uniformity (smallest variation between all responses). So my question is: does this model make sense for what i am looking for?
- Edit: Or should i create only 1 response as the average thickness of 5 locations and another response as the standard deviation and set that to minimize?
Ok i figured so, but i wanted to get around that because there are 5 locations. Which would mean 5 levels and that would make it much more complex. Also, i am coating all 5 locations in the same run. If i make that a factor i wouldn't be able to do that. Any suggestions?