5. Process Improvement 5.5. Advanced topics 5.5.4. What is a mixture design?
When the factors are proportions of a blend, you need to use a mixture design	In a mixture experiment, the independent factors are proportions of different components of a blend. For example, if you want to optimize the tensile strength of stainless steel, then the factors of interest might be the proportions of iron, copper, nickel, and chromium in the alloy. The fact that the proportions of the different factors must sum to 100% complicate the design as well as the analysis of mixture experiments.
	When the mixture components are subject to the constraint that they must sum to one, there are standard mixture designs for fitting standard models, such as Simplex-Lattice designs and Simple-Centroid designs. When mixture components are subject to additional constraints, such as a maximum and/or minimum value for each component, designs other than the standard mixture designs, referred to as constrained mixture designs or Extreme-Vertices designs, are appropriate.
	In mixture experiments, the measured response is assumed to depend only on the relative proportions of the ingredients or components in the mixture and not on the amount of the mixture. The amount of the mixture could also be studied as an additional factor in the experiment, however, this would be an example of where mixture and process variables are treated together.
	The main distinction between mixture experiments and independent variable experiments is that with the former, the input variables or components are non negative proportionate amounts of the mixture, and if expressed as fractions of the mixture, they must sum to one. If the sum of the component proportions are less than one, then the variable proportions can be rewritten as scaled fractions so that the scaled fractions sum to one.
Purpose of a mixture design	In mixture problems, the purpose of the experiment is to model the blending surface with some form of mathematical equation so that:  Predictions of the response for any mixture or combination of the ingredients can be made empirically, or Some measure of the influence on the response of each component singly and in combination with other components can be obtained.
Assumptions for mixture experiments	The usual assumptions made for factorial experiments are also assumed for mixture experiments. In particular, it is assumed that the errors are assumed to be independent and identically distributed with zero mean and common variance. Another assumption that is made, similar to that made for factorial designs, is that the true underlying response surface is continuous over the region being studied.
Steps in planning a mixture experiment	Planning a mixture experiment typically involves the following steps (Cornell, Piepel, 1994):  Define the objectives of the experiment Select the mixture components and any other factors to be studied. Other factors may include process variables or the total amount of the mixture. Identify any constraints on the mixture components or other factors in order to specify the experimental region. Identify the response variables to be measured. Propose an appropriate model form for modeling the response data as functions of the mixture components and other factors selected for the experiment. Select an experimental design that is sufficient not only to fit the proposed model form but allows a test of model adequacy as well.