Augmenting experiments with a DOE, effect on future experiments

dbu

Hello,

I understand that when you enter parameters for an experimental space, a DOE is created to most efficiently explore that space. My question is regarding the effect of previous data on the DOE.

If your previous data has already explored part of the parameter space, will the DOE take this data into consideration to most efficiently explore the rest of the parameter space? Or will the DOE just use the data for fitting, but explore the space as if nothing was known yet?

Thanks for your time

Victor_G

Hi @dbu,

 

Short answer : Yes, the "Augment Design" platform takes into account previous experiments and terms in the model design to suggest new runs that may increase the level of information.

Long answer : The results of the "Augment Design" platform will depend on what you're trying to achieve and the modifications you're doing :

• If you reduce the experimental space, the new points will be generated in this smaller space (see screenshot "Reduction of experimental space" done with space-filling augment design platform).
• If you increase the experimental space, most of the runs will be allowed in this space than in the original one (see screenshot "Augmentation of experimental space" done with space-filling augment design platform).

 

Note that if you check "Group new runs into separate block", JMP will still do some runs in the previous experimental space (unless it is not included in the factors ranges at all) in order to check if this second round of experiments has a similar variance than the first one.

Also the type of augmentation will create new runs differently, depending if you're adding more terms in the model (through the option "Augment"), or if you replicate, add centerpoints/axial or fold over your design, or if you're doing space filling (for this last point, you can see the screenshot done on simulated data "Space-Filling augmentation", which is quite illustrative, or the previous screenshot for augmentation/reduction of the experimental space). More infos here : Additional Examples of Augmentation Choices (jmp.com)

 

I have done a study on this augmentation on a concrete use case (from the former company I have worked for) that you can find here : Réparation d'un plan d'expériences incomplet par augmentation - Réunion du group... - JMP User Community Sorry that it's in french, but you will see the point made and the advantage of augmentation quite easily on the use case : it enables us to use efficiently previous lab data (supposed to be from a DoE, but it wasn't really the case) and augment the initial set of experiments to have a correct DoE with less new experiments needed that if we had to start from scratch. From this use case, you can look at the screenshot done on 3 factors with the scatterplot 3D to see where the new runs are in the experimental space (in green) compared to initial runs (in blue).

Hope this answer will help you,

Space-Filling augmentation.jpg

Use-case_augmentation.jpg

Reduction of experimental space.jpg

Augmentation of experimental space.jpg