A design point in which Parameter values were calculated directly in Simulation, a CAD system, or DesignModeler.

A design point which has parameter values calculated in DesignXplorer. As such, the parameter values are approximate and calculated from response charts.

Optimization method (used in DesignXplorer) where each change of the value of any input variable requires a new finite element analysis. To perform a "what-if study" where several input variables are varied in a certain range, a considerable number of finite-element analyses may be required to satisfactorily evaluate the finite-element results over the space of the input variables. The DOE method generates a response surface using curve and surface fitting algorithms to "fit" output data as a function of input data. This requires a group of design points where each point is generated via a finite element solve.

Those parameters that define the geometry (e.g., length, width, thickness) to the analysis for the structure under investigation.

A single pass through an analysis file.

In each loop, the Six Sigma Analysis tool uses the values of the uncertainty variables from one sample and executes the user-specified analysis. The Six Sigma Analysis tool collects the values for the uncertainty variables following each loop.

A measure of location often used to describe the general location of the bulk of the scattering data of a random output parameter or of a statistical distribution function.

Mathematically, the mean value is the arithmetic average of the data. The mean value also represents the center of gravity of the data points. Another name for the mean value is the expected value.

The statistical point where 50% of the data is below the value and 50% is above.

For symmetrical distribution functions (Gaussian, uniform, etc.) the median value and the mean value are identical, but for nonsymmetrical distributions, they are different.

Parameters that define the response outputs (e.g., volume) from the analysis.

A concept used in multi-objective optimization, especially where some or all of the objectives are mutually conflicting. In such a case, there is no single point which simultaneously yields the "best" value of all the objectives. Instead, the best solutions, called a Pareto or non-dominated set, are a group of solutions such that selecting any one of them in place of another will always sacrifice the quality of at least one objective while improving at least one other.

The empirical relationship between a variable of interest, y, and a set of independent variables, x1, x2, x3, ... Usually the function y = F(x1, x2, x3, . . . ) is a polynomial or some other well-defined relationship, which forms the response surface model of y. When applied to design and analysis in the realm of CAD and CAE, a response surface is the representation of the physical behavior of a structure in terms of its independent variables. For example, a response surface can be devised for the fundamental frequency (F) of a structure as a function of CAD geometry parameters (R, L) and the modulus of elasticity (E); that is, F = G(R,L,E).

The results of a finite element analysis.

The result parameters are typically a function of the uncertainty variables; that is, changing the values of the uncertainty variables should change the value of the result parameters.

A unique set of parameter values that represents a particular model configuration.

A sample is characterized by uncertainty variable values. Think of a sample as one virtual prototype. Every component manufactured represents one sample, because you can measure its particular properties (material, geometry, etc.) and obtain specific values for each.

In statistics, however, sample also has a wider and more general use. For example, any single measured value of any physical property is considered to be one sample. Because a Six Sigma Analysis is based on a statistical evaluation of the result parameters, the values of the result parameters are also called samples.

A measure of variability (i.e., dispersion or spread) about the arithmetic mean value, often used to describe the width of the scatter of a random output parameter or of a statistical distribution function.

The larger the standard deviation, the wider the scatter, and the more likely it is that there are data values further apart from the mean value.

Quantities that influence the result of an analysis.

In a Six Sigma Analysis, uncertainty variables are often called "drivers" because they drive the result of an analysis. You must specify the type of statistical distribution the uncertainty variables follow and the parameter values of their distribution functions.

Optimization method (used in DesignXplorer) that is based on a single finite-element solve, combined with the use of mesh morphing and the Taylor series expansion approximation. The Taylor series expansion handles up to 10 input variables, and can be used with multiple input variables or shape parameters. See Variational Technology for further details.

Bottom frame of left pane. Offers detailed choices when an item is selected from the Views pane and/or View Sub-options.

Top of left pane. The Views pane is used to choose sections of DesignXplorer to view.

Middle of left pane. When an option is chosen in the Views pane, sub-options, if available, will be displayed.