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Augment Design: Enter Numbers of Runs

I am performing materials design with DOE. I already have 150 experimental data. There are more 15 variables. Now I want to add additional runs by Augment Design. I am confused how to determine the Numbers of Runs. How do I know the new experiments from augment design is good or not?

 

PS: I have tried the Numbers of Runs from 300 to 100000, the D-eff. is still low and never reach 100. it can reach 40 at round 8000, but 8000 new experiments are too much. What should I do ?

Re: Augment Design: Enter Numbers of Runs

And one should never forget about actual training with hands-on tutorials, quizzes, etc. You can see that there are several possible options when it comes to design of experiments, including e-learning. Based on your questions, I would recommend the course JMP Software: Custom Design of Experiments.  https://support.sas.com/training/us/paths/jmp.html

Re: Augment Design: Enter Numbers of Runs

Thank you very much

Re: Augment Design: Enter Numbers of Runs

The Mastering JMP webcasts are a good place to see case study examples using JMP. There is a recorded webcast about design diagnostics that should help.

Re: Augment Design: Enter Numbers of Runs

Thank you very much.  phil_kay.

 

I have read the first 3 chapter yet and get a lot of details. However, I am confused what should I do in real case since the book did not show how to do it.

 

Could you tell me how to consider the power to detect the effect. A tutorial or example will be the best.

Re: Augment Design: Enter Numbers of RunsD-eff might not be the most useful design diagnostic for you to use in this case. My guess is that the existing 150 runs have some strong correlation between effects so even when you augment with a lot of new runs the complete design is far from orthogonal.

You should consider the power to detect effects. You can probably get a good estimate of experimental "noise" from your existing 150 runs. Then you just need to decide what size of effect it is important for you to detect.

I would recommend that you read Optimal Design of Experiments: a case study approach by Goos and Jones as this goes into a lot of detail about many of the design diagnostics.