About the DOE Techniques

About the Adaptive DOE Technique

The Adaptive DOE Technique is a single run of a space-filling DOE technique that fills the space with a set of design points generated in such a way so as to maximize the minimum distance between neighboring points. You can optionally provide a data file containing a set of points to be avoided by the space-filling algorithm.

For more information, see Adaptive DOE Technique.

About the Box-Behnken Technique

Box-Behnken designs are a class of incomplete three-level factorial designs consisting of orthogonal blocks. These designs either meet, or approximately meet, the criterion of rotatability. They are typically used to estimate the coefficients of a second-degree polynomial. Since Box-Behnken designs do not include any extreme (corner) point, these designs are particularly useful in cases where the corner points are either numerically unstable or infeasible. Box-Behnken designs are available only for three to twenty-one factors.

For more information, see Box-Behnken Technique.

About the Central Composite Technique

The Central Composite Design technique is a statistically based technique in which a 2-level full-factorial experiment is augmented with a center point and two additional points for each factor (called “star points”). Although Central Composite Design requires a significant number of design point evaluations, it is a popular technique for compiling data for response surface modeling because of the expanse of design space covered and the higher-order information obtained.

For more information, see Central Composite Design Technique.

About the Data File Technique

The Data File technique provides a convenient way for you to define your own set of trials outside of Optimization Process Composer and still make use of integration and automation capabilities. The design matrix can be defined by data imported from one file, allowing you to run the DOE study (automatically evaluate all the design points) and analyze the results. Any file used must contain a row of tab, comma, or space separated values for each data point and a column for each parameter to be used as a factor from that file.

About the Fractional Factorial Design Technique

A fractional factorial experiment is a certain fractional subset (1/2, 1/4, 1/8, etc. for two-level factors and 1/3, 1/9, 1/27, etc. for three-level factors) of the full factorial experiment that is carefully selected to minimize aberrations in the experiment. Fractional factorial designs are available only when all factors have either two or three levels. Fractional factorial experiments are also useful when some factors are independent of each other or when certain interactions can be neglected.

For more information, see Fractional Factorial Design Technique.

About the Full Factorial Technique

In a full-factorial design all combinations of all factors at all levels are evaluated. Typically, the standard engineering practice is to systematically evaluate a grid of points requiring $n_1\times n_2\times n_3\times \dots n_i$ ($i$ = # factors, $n_i$ = # levels for factor $i$) design point evaluations. This practice provides extensive information for accurate estimation of factor and interaction effects. However, it is often deemed cost-prohibitive because of the number of analyses required.

About the Latin Hypercube Technique

The Latin Hypercube technique is a class of experimental designs that efficiently sample large design spaces. The design space for each factor is divided uniformly (the same number of divisions, $n$, for all factors). These levels are randomly combined to specify $n$ points defining the design matrix (each level of a factor is studied only once).

For more information, see Latin Hypercube Technique.

About the Optimal Latin Hypercube

The Optimal Latin Hypercube technique is a modified Latin Hypercube where the combination of factor levels for each factor is optimized, rather than randomly combined. The design space for each factor is divided uniformly (the same number of divisions, $n$ for all factors). These levels are randomly combined to generate a random Latin Hypercube as the initial DOE design matrix with $n$ points (each level of a factor studies only once).

An optimization process is applied to this initial random Latin Hypercube design matrix. By swapping the order of two factor levels in a column of the matrix, a new matrix is generated and the new overall spacing of points is evaluated. The goal of this optimization process is to design a matrix in which the points are spread as evenly as possible within the design space defined by the lower and upper level of each factor.

For more information, see Optimal Latin Hypercube Technique.

About the Orthogonal Array Technique

Orthogonal arrays are a specific type of fractional factorial experiment carefully selected to maintain orthogonality (independence) among the various factors and certain interactions.

It is this orthogonality that allows for independent estimation of factor and interaction effects from the entire set of experimental results. Using orthogonal arrays for fractional factorial design reduces the analysis result resolution (that is, factor effects are aliased with interaction effects as more factors are added to a given array); however, the significant reduction in the required number of experiments (cost) can often justify this loss in resolution as long as some of the interaction effects are assumed negligible. Automation of this procedure allows you to efficiently and effectively study the design space with little or no knowledge of orthogonal arrays.

For more information, see Orthogonal Arrays Technique.

About the Parameter Scan (Main Effects Screening)

The term “Parameter Scan” is used to refer to a true study of the sensitivity of the design to each factor independent of all other factors. Each factor is studied at all of its specified levels (values) while all other factors are held fixed at their baseline. Because interaction effects are varied independently, they are not accounted for when the effects of factors on responses are reported. You can view the main effects using Results Analytics.

For more information, see Parameter Scan Technique.

About the Sobol Sequence Technique

The Sobol sequence technique provides a space filling collection of points that are highly uniform in their spacing, as defined by measures of discrepancy. The uniformity of this technique generally improves on that of the Latin Hypercube technique, with similar cost in generation time. The Optimal Latin Hypercube technique provides greater point uniformity but at an increased cost in generation time; this cost that can become prohibitively high for large numbers of points and factors.

For more information, see Sobol Sequence Technique.