@Victor_G - You can record the order of the experiments in the table and see if you have an effect of the experiment order on the response.
@bdclark23 - I get this question often and provide this from Montgomery to my clients: Randomization is the cornerstone underlying the use of statistical methos in experiment design. By randomization we mean that both the allocation of the experimental material and the order in which the individual runs of the experiment are to be performed are randomly determined. Statistical methods require that the observations (or errors) be independently distributed random variables. Randomization usually makes this assumption valid. By properly randomizing the experiment, we also assist in “averaging out” the effect of extraneous factors that may be present.
Video 2: Augment Design (Sequential DOE) - John Szarka ( @jszarka )
Augment DOE 2.mp4
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Question fromThalita Coppini Soares ( @THCSCould you run different ranges of the variables from the 1st experiment in this scenario?
Answers:
@Jonas_Rinne - Yes you can change that as well if needed.
@Victor_G - Yes, you can change the ranges of the factors. This enables to focus in the optimal area (reduce the range, restrict the experimental space) or move the design space a little further if you have no interesting area to search/optimize for
@Paul_J - Or maybe adding a new factor with the next set?
Question fromThalita Coppini Soares ( @THCSso, start with a PB to reduce the number of experiments, and then run a full design just with the main factors with narrow ranges?
Answer
@Victor_G - Or with another screening design, in the example here DSD could be helpful: no constraints and high number of continuous factors, so it could help better estimate main effects and possibly detect interactions and quadratic effects.
And once your screening is done, why not adding higher order effects (interactions, quadratic effects, ...) in the augmentation on the narrow ranges to expand your knowledge about the system and have a better/more accurate model?
Video 3: Is it reasonable to keep main effects in the DoE Model even when they're not significant? How reliable is the effect size estimation of these "insignificant" factors? - Johann-Christoph Dettmann ( @JohannD124 )
reasonable to keep main effects in the DoE Model.mp4
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Design of experiments and machine learning with application to industrial experiments
Statistical Papers - In the context of product innovation, there is an emerging trend to use Machine Learning (ML) models with the support of Design Of Experiments (DOE). The paper aims firstly to...
