topic Re: Recordings DOE Club Q3 2024 - Avoid Pitfalls/ Mistakes using DSD/ Taguchi experiments in Design of Experiments Club Discussions

Recordings DOE Club Q3 2024 - Avoid Pitfalls/ Mistakes using DSD/ Taguchi experiments

maria_astals — Wed, 25 Sep 2024 09:33:44 GMT

Question 2: How to avoid Pitfalls

(view in My Videos)

Comments

@Victor_G :

1. Do not assume any conclusions/interpretations outside of your design space

2. And validate the model(s) with validation points!

@Stats_Like_Jazz :

Lesson 1: be brave when setting ranges.

Lesson 2 : there might be a lot of "good" models for your response(s). Choose a relevant criterion (or several ones) linked to your objective(s) to evaluate and select model(s).

Lesson 3: if you have ranges or combination to avoid use the disallowed combination filter.

I have a 4 minute video on how to do just that: https://youtu.be/0ILaduCaszE

@ryan_cooper:

I'd say another pitfall (particularly for beginners) is overthinking the DOE. Anything is better than one-factor-at-a-time. You can build on your DOE knowledge as you do more of them - you don't have to know everything to get started.

Question 3: Mistakes using Definitive Screening Designs (DSD)

(view in My Videos)

Comments:

@Jonas_Rinne

Do not try to learn everything in the first try. People trend to try to bring everything in in the first design. Most big designs do not work without a lot of prior knowledge.

@Victor_G

Also think iteratively. It's very rare to have all responses and conclusions with only one design.

@KimRas2

Not adding enough potential factors

@Steph_Georges

Not Randomizing Runs: If the order of experimental runs is not randomized, lurking variables (such as time or temperature drift) may introduce bias into the results.

Here is an article from Bradley Jones about "Proper and Improper use of DSD": https://community.jmp.com/t5/JMP-Blog/Proper-and-improper-use-of-Definitive-Screening-Designs-DSDs/ba-p/30703

Question 4: Moving from DoE to Taguchi experiments - robust design

(view in My Videos)

Comments:

@Victor_G

Depending on where/how you want to find robust process design, there may be better alternatives today with Optimal designs.

And if you have several 2-levels categorical factors, try using OML designs instead: Mixed-Level Screening Designs (jmp.com)

@KimRas2

IMH, Taguchi is a less modern form of DoE, where you have to buy expensive books on stadardized DoE designs.

Re: Recordings DOE Club Q3 2024 - Avoid Pitfalls/ Mistakes using DSD/ Taguchi experiments

Victor_G — Wed, 25 Sep 2024 20:03:13 GMT

Thanks for the upload and adding the comments @maria_astals !

Some complementary comments and informations from my side :

Question 2: How to avoid Pitfalls

Besides the excellent points mentioned in the discussion and comments, the article "Eight Keys to Successful DOE" from Quality Digest really summarize and explain well the most important aspects to consider when building designs :

Set good objectives
Measure responses quantitatively
Replicate to dampen uncontrollable variation (noise)
Randomize the run order
Block out known sources of variation
Know which effects (if any) will be aliased
Do a sequential series of experiments
Always confirm critical findings

Question 3: Mistakes using Definitive Screening Designs (DSD)

A specific analysis platform for DSD, The Fit Definitive Screening Platform, has been developed to leverage the specific foldover structure of the DSD to make the analysis easier and enable the detection of 2-factors interactions and quadratic effects following the principle of effect heredity. This analysis can be extended and used to any design with a foldover structure, not only for DSDs, see article from Bradley Jones :

However, even if this analysis method is very powerful, it may happen that some terms are not detected even if they may be active, due to low power for higher order effects and possible low effect size, example :
Other platforms may be helpful like The Fit Two Level Screening Platform or Generalized Regression Models (with different estimation methods like Best Subset, Pruned/2-stage/ Forward selection, ...), as they can help you build different models, using different methods to detect active effects and estimate parameters.

As the saying goes "All models are wrong, some are useful", so the key is really to compare and evaluate different models to get a good understanding of the system. Different methods for selecting a model can yield different solutions. Choosing a relevant evaluation metric to compare the different models (depending on the objective), compare the different models and selecting and/or combining different modeling methods with domain expertise can help you have a broader and more precise view about what matters the most. And from then, you can plan your next experiments to refine your model, and/or prepare some validation points to assess your model performance.

(I have submitted an abstract for JMP Europe Discovery 2025 using a Mixed-Level Screening Design and the different modeling platforms mentioned for the analysis of the responses, I hope to be selected to present this platforms and models comparison).

Question 4: Moving from DoE to Taguchi experiments - robust design

There may be several definition of robustness and different ways to create a robustness DoE, depending against which factors you want to test the robustness of your experimental space/optimum point : against external noise factors, against variation in process/experiment factors, or against a combination of both factors. Here is a serie of articles from Stat-Ease explaining the type of designs according to which robustness situation you are interested in :

Robustness against external noise factors : https://statease.com/blog/achieving-robust-processes-via-three-experiment-design-options-part-1/
Robustness against variation in our set points for process factors : https://statease.com/blog/achieving-robust-processes-via-three-experiment-design-options-part-2/
A combination of the first two types : https://statease.com/blog/achieving-robust-processes-via-three-experiment-design-options-part-3/

Some ressources that may be helpful as well :

Designing Robust Products (2020-US-30MP-539)

How to Design for Robust Production Processes | JMP

Hope these info may help JMP users, happy experimentations !

Re: Recordings DOE Club Q3 2024 - Avoid Pitfalls/ Mistakes using DSD/ Taguchi experiments

statman — Fri, 11 Oct 2024 21:29:58 GMT

I thought I joined the club, but was not informed of the "meeting"?