DOE Methods
Numerical methods available for a DOE approach.
| Method | Type | Input Variable Levels | Basic Parameters | Properties and Comments |
|---|---|---|---|---|
| Box Behnken | Space Filling | 3 | Click Apply for AutoSelect or select a table using the Design pull-down menu. | Use to build quadratic response surfaces if the responses are
known to be quadratic and predictions are not required at the
edge of the design space. Number of points can be 13, 25, 41,
49. 57. Selecting Autoselect will pick bbdgn13 if N < 4, where N is the number of design variables; bbdgn25 if N = 4, bbdgn41 if N = 5, etc. Limited to 7 design variables. Discrete variable must have at least 3 levels. Categorical variables must have exactly 3 levels. |
| Central Composite Design (CCD) | Space Filling | 5 | Use when the responses are known to be quadratic. Limited to 20 design variables. |
|
| D-Optimal | Space Filling | Any | You can either accept the default number of runs or enter a different value. You can also select the appropriate regression model. | Use when the known goal is to build a regression. This method is also useful when corner coverage is important, and you have problems with input variable constraints. |
| Fractional Factorial | Screening | Any | Select the appropriate resolution. | Resolution indicates the level of accuracy of the
interactions. Interactions should not be used with Resolution III. |
| Full Factorial | Screening | Any | Requires a high number of simulations and is therefore
unsuitable for most studies. Total number of runs should be less than 1,000,000. |
|
| Hammersley | Space Filling | Any | You can either accept the default number of runs or enter a different value. | Use when the response surface is highly nonlinear. This method is a better space filler than Latin HyperCube. The default number of runs is 1.1*((N+1)*(N+2))/2, where N is the number of design variables. |
| Latin HyperCube | Space Filling | Any | You can either accept the default number of runs or enter a different value. | Use when the response surface is highly nonlinear. The default number of runs is 1.1*((N+1)*(N+2))/2, where N is the number of design variables. You must maintain the value of the random seed in order to get repeatable designs. |
| Modified Extensible Lattice Sequence (Mels) | Space Filling | Any | You can either accept the default number of runs or enter a different value. | Use when the response surface is highly nonlinear. This method is a better space filler than Latin HyperCube. The default number of runs is 1.1*((N+1)*(N+2))/2, where N is the number of design variables. |
| Plackett Burman (PB) | Screening | Any | Computationally least expensive. Number of points can be 12, 20, 24, 28 or 36. Selecting Autoselect will pick pbdgn12 if N < 12, where N is the number of design variables; pbdgn20 if 12 <= N < 20, etc. Limited to 35 design variables. Categorical variables must have exactly two levels. |
|
| Run Matrix | Custom | Any | Select the perturb file. | Use to create a design matrix using literal variable values. |
| Taguchi | Screening | Varies | You can either choose AutoSelect or a specific design matrix. | The levels of each variable must be set accordingly to ensure compatibility with a specific design matrix. |
| User Defined Design | Custom | Any | Select the perturb file. | Use to create a design matrix using abstract variable levels. |