1 Select DOE > Classical > Mixture Design.
 2
 3 Set the ranges for the five factors as shown in Ranges for Five-factors and click Continue.
Ranges for Five-factors
 4 Enter 4 in the Degree text box and click Extreme Vertices.
Display and Modify Panel for Extreme Vertices Example
 5 (Optional) To match the output of this example, click the Mixture Design red triangle and select Set Random Seed, and then enter 1409.
 6 Enter 10 in the Choose desired sample size box and click Find Subset to generate the design.
Note: The Find Subset option uses the row exchange method (not coordinate exchange) to find the optimal subset of rows.
Ten Run D-optimal Extreme Vertices Design
 7 Click Make Table.
 8 From the design table, select Graph >Ternary Plot.
 9 Select X1, X2, X3, X4, and X5 and click X, Plotting, and then click OK.
Partial Output of Ternary Plot for Five-Factor Design
 1 Select DOE > Classical > Mixture Design.
 2 Enter the values from Values and Linear Constraints for the Snee and Piepel Example for X1, X2, and X3 and click Continue.
Values and Linear Constraints for the Snee and Piepel Example
 3 Click Linear Constraint three times. Enter the constraints as shown in Values and Linear Constraints for the Snee and Piepel Example.
 4 Click the Extreme Vertices button.
 5 Click Make Table.
 6 From the design table, select Graph >Ternary Plot.
 7 Select X1, X2, and X3 and click X, Plotting, and then click OK.
Diagram of Ternary Plot Showing Piepel Example
The XVERT method first creates a full 2nf 1 design using the given low and high values of the nf – 1 factors with smallest range. Then, it computes the value of the one factor left out based on the restriction that the factors’ values must sum to one. It keeps points that are not in factor’s range. If not, it increments or decrements the value to bring it within range, and decrements or increments each of the other factors in turn by the same amount. This method keeps the points that still satisfy the initial restrictions.
The above algorithm creates the vertices of the feasible region in the simplex defined by the factor constraints. However, Snee (1975) has shown that it can also be useful to have the centroids of the edges and faces of the feasible region. A generalized n-dimensional face of the feasible region is defined by nf – n of the boundaries and the centroid of a face defined to be the average of the vertices lying on it. The algorithm generates all possible combinations of the boundary conditions and then averages over the vertices generated on the first step.

Help created on 9/19/2017