See how to:

Understand three families of designs and when you might use them: optimal, classical and modern (space filling)

Create a mixtureprocess experiment for mixtures with a continuous variable (process factor) using Custom Design

Create a mixtureprocess experiment using Space Filling Design

Contrast Custom and Space Filling Designs
Questions answered by Scott @scott_allen and Wendy @wendytseng during the live webinar:
Q: What are differences among types of optimality (I, D, A and G)?
A: The different optimality criteria cover the design space differently. You can see a comparison between D and I optimal design space coverage in the
JMP Documentation1 and JMP Documentation2.
Q: Is nonlinearity introduced through interaction terms?
A: In a design with specified model terms, you can estimate nonlinear blending effects with interactions and
Scheffé cubic terms.
Q: How do we consider subcomponents of an ingredient (raw material) in a mixture design. for example an ingredient which is made of 2 sub components?
A: There might be a few ways to include this in a model. If you had mixture component A, that was made up of components a1 and a2; you could include a continuous factor describing the ratio of a1/a2.
Q: In the example, do you also need the proofing time main effect?
A: When you include a process factor/mixture factor interaction, the process factor main effect is removed from the model.
Q: Could you please comment on the analysis models used for space filling and Scheffé cubics? I was under the impression that standard linear models won't work and we must use gaussian or other models.
A: When you specify model terms (including 2way interactions or Scheffe cubic) in your design, you can use standard linear least squares modeling methods. If you generate a spacefilling design, you may need to use other models, like Gaussian or Neural Net, or some specialized modeling in Generalized Regression.
Q: Is there an easy way to see how increasing my run budget would improve statistical rigor or power of the model?
A: Power of mixture designs is difficult to estimate because of the correlated factors. Design space coverage is a way to visualize this. Whether or not the points in your design are spread throughout the design in the regions where you want to the information.
Q: Does JMP do pseudocoding under the hood, or do you need to specify that somewhere?
A: JMP does it. From Column Info, you can specify the type of psuedocoding. If you are analyzing an experiment that has not been done in JMP, you will need to make sure the column properties are set.
Q: How did you ask the software to pick the factors to give the tallest bread?
A: Under the Prediction Profiler red triangle, I Set Desirabilities, and then from the red triangle Maximize Desirability.
Q: Why do you keep interactions in the effects summary with p value of 0.7?
A: We are just working with the model as provided without reducing it. You could certainly move terms in and out to see how that effects your analysis.
Because the model terms are highly correlated, though, you might choose to keep those model terms that do not appear significant and validate with confirmation experiments.
Q: How do you visualize mixture design spaces with more than three components?
A: These are commonly visualized with ternary plots with two of the three vertices representing individual components and the third vertices representing the sum of all other components. Here is an example of the Lubricant Formulation experiment that is in Strategies for Formulations Development, A StepbyStep Guide Using JMP (Snee and Hoerl, 2016). Look at the first ternary plot at the top, Others = % Acid B + % Acid C.
Ternary Plot Example Snee and Hoerl
Here is a short video that explains how to visualize design spaces for mixtures with more than three components.
Resources