Hosted by @HadleyMyers , DOE enthusiasts shared discusions about several topics.
Feel free to continue the discussions below!
Video 1: Introduction
Video 2: Dealing with outliers
Comments:
@Victor_G : And also separate repetitions from replications to better understand if the problem comes from measurement process (repetitions), or from the system (with possible uncertainty in fixing factors levels, seen through replications)
Video 3: Uses of covariate factors
Comments:
@Victor_G : Jed Campbell from JMP did a great blog post about non-linear factors constraints : https://community.jmp.com/t5/JMP-Blog/Demystifying-Factor-Constraints/ba-p/619898
Demystifying Factor Constraints
Isn't Define Factor Constraints something I should just leave alone or pretend doesn't exist? A wise man does nothing by constraint. -Marcus Tullius Cicero If you've felt intimidated by the Define...
@Paul_J : Did that all the time when I worked in food and chem industries.
@Bill_Worley : I'm a big proponent of using covariate candidate sets.
@Victor_G : Covariate sets are very useful in chemistry, when dealing with chemical properties as inputs for example
Video 4: best practices on setting ranges
Comments:
@Victor_G : Use the broad range in screening (possibly with centre points), and refine the range in exploration/optimization.
@KimRas2 : Be bold!
@shs: What works for me, if you start from scratch and you are unsure if your borders work, start with the experiment which you think might be the most critical. Here you waste only one experiment.
@Victor_G : Scoping designs are also very useful to define appropriate ranges and check variability in design space : https://prismtc.co.uk/resources/blogs-and-articles/scoping-designs
You only try tests with -1 levels for all factors, centre point(s) and +1 levels for all factors.
And you can very easily augment your scoping design into a factorial design
@Nicola_Nuti: Agreed, increasing effect size gives more power as well.
@Jonas_Rinne : The idea of scoping designs is super helpfull to check if your ranges make sense. Especially when you have many factors.
@SDF1: Even trials that don't work still provide very useful information for the DOE and the whole exploratory process.
@shs : With too narrow settings, you can never be sure about curvature., which is often the case with temperature.
@Paul_J : setting the ranges is good example of why to keep the science in the stats.
@Bill_Worley : If you have some historical data you can build a model and use the Design Space Profiler to help determine optimal ranges.
@Victor_G : Yes, DoE is a method change, but with more predictability about the outcomes, learnings and with planning made a lot easier (compared to trying One Factor At A Time and relying on best guess).
@SDF1 : Depending on how "new" the research is, sometimes a "pre-DOE" is necessary to determine what are the workable ranges for the factors that you're testing so that when you actually do sit down to create a more detailed/complex DOE, you're more likely to have all runs be successful.
Video 5: Modern DOE´s
@Victor_G : Really like the metaphor with the roof, thanks for sharing!
@shs : I rarely use classical design anymore, as they are large in experimental runs. Using concepts like SVEM with small designs, or definite screening designs, etc.. can give you designs which can cover large amount of factors with few runs, and that convinces people when they first see how much the experimental efforts can drop using modern designs.
@Victor_G :
I often evaluate and compare modern designs with a possible "classical" counterpart, to better show the benefit of the modern design
@KimRas2 : Functional DoE?
@ryan_cooper : I often like to refer to some of Phil Kay's points in this Community discussion. (The title of the question is misleading, however.) Some discussion on why most things can be done with DSD and Custom design.
https://community.jmp.com/t5/Discussions/Reason-against-using-Custom-Design-DOE-for-everything/td-p/...
Video 6: Functional Data Explorer
@Victor_G : Functional DoE with curve output : https://community.jmp.com/t5/Discovery-Summit-Europe-2021/Use-of-Functional-Data-Explorer-in-a-Mixtu...
You can use curve data (functional data) as inputs and/or output.
Lyndal asked: Does the standard JMP license cover functional DoE analysis? I thought that needed JMP Pro?
Yes, it´s only in Pro
@Victor_G : As an input, you can use coefficients from functional analysis, and link these coefficients to your outputs
@Phil_Kay : @Ben_BarrIngh has some expertise in that field. Ben will be at Discovery Summit in Manchester if you able to make it.
