1.

Select DOE > Classical > Mixture Design.

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Note: The Find Subset option uses the row exchange method (not coordinate exchange) to find the optimal subset of rows.
7.

Click Make Table.

8.

From the design table, select Graph >Ternary Plot.

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When you have more than three mixture components, the ternary plot shows a series of plots. Each plot has two component axes and the third axis is the sum of all other components. The plots are shaded if you have a constrained region. The unshaded portion represents the feasible region. For details about ternary plots see Ternary Plot Overview.
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.

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.

The ternary plot shows the feasible region as defined by the factor limits (the linear constraints are not considered in the shading). The design points are at the six vertices of the feasible region, at the six edge midpoints, and the overall centroid. For details about ternary plots see Ternary Plot Overview.
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 ndimensional 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.