Provides a cost-effective methodology for the evaluation of factors whose sum total volume or quantity cannot change. For example, if you wish to add more fruit filling to an 8-ounce fruit bar, another ingredient must be reduced. Such adjustments are common in packaged food and chemical formulations. The goals of this type of experiment are usually focused on developing a full predictive model (Y = f(X)) describing how the ingredients in the mixture jointly affect the process output and determining the optimal amounts of each ingredient.
When to Use | Purpose |
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Mid-project | If you believe the desired characteristics of the mixture are a function of only the ingredients, use a pure mixture DOE to evaluate which ingredients have the largest influence on the characteristics, build a predictive model using the key ingredients, and find the optimal quantities of the ingredients. |
Mid-project | If you believe the desired characteristics of the mixture are a function of both the ingredients and the process, use a mixed model DOE (some factors are ingredients, some are process inputs) to evaluate which ingredients and process inputs have the largest influence on the characteristics. Then, build a predictive model using the key ingredients and key process inputs and find the optimal quantities of the ingredients along with the optimal settings of the process inputs. |
Continuous Y, continuous X's (for a pure mixture design).
If you have discrete numeric data from which you can obtain every equally spaced value and you have measured at least 10 possible values, you can evaluate these data as if they are continuous.