Which design to choose

[#1]

Dear All,

I am planning to build an RSM to optimize my method. I have 8 continuous factors and 2 responses. The levels of each factor were determined according to some internal methods and previous experiences of the team. I have no idea if there are interactions between factors or not. I can not identify the source of noise and I am planning to randomize

After a small research, I think they are two ways to do this, and I do not know which one is more suitable?
1) sequential experimentation using Central Composite Design (Box-Wilson) because I can run a classical screening design with some center point and continue if needed with axial points.

2) Use a Placket Burman design to screen the main effect and then a central composite design. And this way is the most used in my field

thank you

[#2]

Thank you for your quick answer. As I said last time my factor is the number of cycles that can take the levels 1,2 and 3. Using the DSD I am planing to do a screening followed by a response surface.
My concern if I consider the factor as continuous, how the results will be expressed? for example, if I want to maximize my response the software will give a value of 1.2 for my factor what should I do then? consider it as 1 ?
best regards

[#3]

If you follow the recommended DSD analysis path, the only catch is to make sure you do not estimate the squared term for that factor. When it comes to using the profiler, change the modeling type to ordinal or nominal before using the profiler. That will keep the values to 1, 2, or 3.

[#4]

thank you @Dan_Obermiller . How about setting this factor as 2-levels categorical and all the other factors as 3 level continuous factors?

best regards 

[#5]

For any categorical variable you could always remove a level, if desired. But that means you won't have any information on the missing level.

For a DSD (or custom design), you do not specify the number of levels for a continuous factor. Instead, the number of levels are chosen in order to estimate the model. If your model has squared terms (which response surface models do), then you will have 3 levels for continuous factors. Since DSDs are designed to screen and possibly fit a response surface model, all continuous factors will automatically get 3 levels.

Check out the JMP Help system on definitive screening designs to get some more background on how these designs work and are created: JMP Definitive Screening Design Information