Mixture DOE

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    I have been tasked with performing a Mixture DOE & i could do with some advice. I’m familar with factorial DOE’s but not mixtures. I’ve done some research in the types of MDOE-simplex centroid, lattice, extreme vertices, but im still unsure about the approach.

    The experiment would be based on a mix that is coated on to paper to enable a good print quality. I have 5 critical X’s ( 3 binders & 2 pigments). For a typical 1000 kg mix Pigment A is 600 kg, pigment B 350 kG, Binder A 20Kg, Binder B 20Kg & binder C10 Kg. This is just an example of 1 mix, other mixes have varying proportions of the different components.

    The critical Y’s im looking at are smothness, pick, gloss & whiteness of the coating. I’m trying to rationalise the number of mixes produced, possibly reduce costs on the expensive pigments & create predictive models for my critical Y’s.

    I’m using Minitab & have experimented with some extreme vertices designs. Could anyone give some advice on this matter.





    For a mixture design, that’s a fair number of variables. I don’t know what your cost is, but if you’re doing this in 1000Kg batches, the exercise will probably not cheap.

    Have you looked into a simplex method or some variation on that idea? It’s less structured, but may work in this situation.



    You definitely need to go small scale first. You also need to understand your constraints. If you have no upper bounds on your components you could use a simplex design and save some runs over your extreme vertices design. You also need to determine what relationship model you suspect you need to fit. You can screen first with a lattice design of degree 3 or 4 and then go with a higher degree when you get some relative operating ranges and eliminate some of the factors. With 5 components you need to really understand your goals and have some science to point you in a good direction before choosing the specific design

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