Creating Robust Parameter Designs

To create a robust parameter design, follow the steps outlined next. For more general information on building designs, see Building a New Design.

Choose Insert > DOE > Add Robust Design to add a robust design folio to the current project.

If you wish to rename the folio, click the heading at the very top of the navigation panel and edit the text in the Name field in the input panel.

Configure the inner array, which consists of the control factors, as follows:

Select Design Type in the Inner Array section of the folio's navigation panel, and then specify the design type that you want to use for the inner array in the input panel. You can use any factorial design type for the inner array. Note that most Taguchi orthogonal arrays are saturated and therefore not effective for examining factorial interactions. If interactions of the control factors are likely, using a two level factorial design for the inner array may be desirable.

Define the factors. To edit the first factor, click Factor 1 in the navigation panel and then edit its properties in the input panel. To add a factor, choose Design > Factors > Add Factor.

The number of factors you can have and the number of levels each factor can have depend on the design type that you have selected.

Two level factorial: You can have 2 to 15 factors, and each factor must have 2 levels.
Plackett-Burman: You can have 2 to 47 factors, and each factor must have 2 levels.
General full factorial: You can have 2 to 7 factors, and each factor can have 2 to 20 levels.
Taguchi OA factorial: The number of factors that can be considered is dependent on the factor levels used. (See Available Taguchi OA Designs.)

Click Additional Settings in the navigation panel and specify the settings described next.

For two level factorial designs:
- Fraction allows you to select the number of base runs you want to use in the experiment, based on a fraction of the total number of runs that would be required for a full factorial design. For example, if your design includes five factors with two levels each, a full factorial design would require 25 = 32 base runs. For a fractional factorial design in this case, you can select to use a one-half fraction (16 runs) or a one-fourth fraction (8 runs). The total number of runs required for the experiment may be greater than the number of base runs (e.g., if you use replicates and/or center points).
- Fraction Generators allows you to specify the factorial interaction effects that will be aliased with the main effects of particular factors. (See Specifying Generators.)
For Plackett-Burman designs:
- Base # of Runs allows you to specify the number of runs you want to use in the experiment. The available choices are determined by the number of factors you specify and the design matrix developed by Robin L. Plackett and J. P. Burman.
For general full factorial designs, no additional settings are necessary.
For Taguchi OA designs:

The options available in the Taguchi Design Type drop-down list will depend on the numbers of levels for each factor. For example, if all your factors have three levels, then you could choose the L9 (3^4) design, which uses nine runs to investigate up to 4 three-level factors. If some factors have two levels and the rest have four, then you could choose the L16 (2^9*4^2) design, which uses 16 runs to investigate up to 9 two-level factors and up to 2 four-level factors.

Click the View Alias Table icon to see the alias table for the selected Taguchi design type. The table shows what aliases result from assigning factors to particular columns in the orthogonal array.

See Taguchi OA Alias Table.

Taguchi Column Indices allows you assign factors to columns of the selected orthogonal array. No two factors can have the same index.

The Specify Interaction Terms link opens a window that helps you assign factors to columns without aliasing any main effects or any interaction terms of interest. (See Specify Interaction Terms Window.)

Note that you cannot define blocks, center points, replicates, responses or repeated measurements for the inner array, regardless of the design type.

Follow similar steps to configure the outer array, which consists of the noise factors. Note the following:

You can use a two level factorial design, a Placket-Burman design or a general full factorial design.
Each of these design types can have 1 to 15 factors when used for the outer array.
In addition to the settings described for configuring the inner array, you can also specify a number of replicates in the Additional Settings for the outer array.
You cannot define blocks, center points, responses or repeated measurements for the outer array, regardless of the design type.

Click Design Summary in the navigation panel to review the properties of your currently configured design. Some of these properties are also listed in the Design Information area on the control panel, and you can access the settings for those properties by clicking the links. (See Design Summary.)

The Design Summary for robust parameter designs includes a Noise Condition Matrix, which displays the values of the noise factors that are used for each noise condition. This matrix is used in understanding how to enter the measured response data on the Data tab. (See Using Robust Parameter Designs.)

Once you are satisfied with your settings, click the Build icon on the control panel to create the Data tab that contains the test "plan."