Chapter 2
Using DesignXplorer 12.0
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Parameters from CAD
Training Manual
• DesignXplorer can utilize user-selected parameters from supported
CAD systems. To expose CAD parameters, a special naming
convention should be used* (the same conventions apply to
DesignModeler).
– A “personal parameter key” is used to flag CAD parameters to be
exported to Workbench. The default parameter key is “ds”.
• Example:
– To expose a CAD parameter named “Length” in Workbench, it could be renamed as
“dslength”, Lengthds”, “ds_Length”, “Length_ds”, etc. (the order is arbitrary as is
the underscore and the case).
– The parameter key is user controlled and can be modified by the user from the
“Geometry Import” section of the options.
* Note: leaving the personal
parameter key field blank will
cause all CAD parameters to be
passed to Workbench.
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Parameter Set…
Training Manual
• Parameter Set is global throughout
the WB applications that support
parameters.
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Parameter Set …
Training Manual
Table of Design Points for What-If analysis
• To build “What-If” studies double click on Parameter Set and the
Table of Design Points becomes available.
• The initial values from the simulation run are listed and the user can
enter new values by clicking on the “*” button.
• Alternatively, users can copy and paste values from Excel spread
sheet.
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Parameter Set …
Training Manual
Each new “Add” becomes a design point.
When all design points are entered choose to
“Update all Design Pints.”
Status bar gives
more info about
design points
update.
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Parameter Set …
Training Manual
• Users can also choose to run a selected design point from the list by
RMB on the design point “Update Selected Design Point.”
• Users can set up selected design point as Current by “Copy input to
Current”, automatically Mechanical application will be updated to selected
design point.
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Parameter Set …
Training Manual
• Parameters Charts allow users
to configure and plot Input vs.
Output in XY plots.
• Select input and output parameter and on the
Toolbox select Parameter Chart to show the
chart output vs. input parameter.
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Design Points
Training Manual
• Design Points:
– A Design Point is a location within the response space which is a
result of a specific combination of input parameters.
– A simple example:
• What is the effect on the first natural frequency of a 25 x 25 mm cantilever
beam when the length is varied from 360 to 440 mm?
400 mm
• Intuition tells us that as the length increases, the natural frequency will
decrease, and the results of a DX solution confirms this (next page).
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Design Points
Training Manual
• Here the graph from DX shows 1st
frequency decreasing as length
increases.
• To develop this response curve DX
ran 5 complete solutions using 5
different values for the “Length_ds”
parameter (360, 380, 400, 420 and 440
mm).
• These solutions are indicated by the
symbol
in the figure at the right.
• The graph between these solutions is
curve fit using a regression analysis
technique (see the documentation for
more details).
• Thus, choosing any value within the
stated range for Length_ds and
evaluating the response (frequency),
represents a “Design Point”.
Design point
(typical)
Automatic
solutions
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Design Points
Training Manual
• Any design point that is chosen from
the response chart (other than the
automatic design point) is a response
design point.
– Response design points represent
approximations based on the
response charts. It is good practice to
generate a hard design point before
proceeding to develop or modify a
design based on a response design
point.
Response Design
point (typical)
Automatic
design point
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Design Points
Training Manual
• Design points are generated based on the number of input
parameters in the system. Design points can be previewed by
clicking the corresponding Preview button in the toolbar.
• Output parameter can be calculated for the generated design points
in several ways:
– From the workspace, click the corresponding Update button in the
toolbar to solve for all of the generated design points.
– From the Project Schematic, right click the cell and select Update from
the menu.
– From the Project Schematic, click the Update Project button in the toolbar
to update all of the systems in the project.
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Design points
Training Manual
Additional Sampling Methods
Optimal Space Filling Design
allows you to fill the design space
efficiently with as few number of
points as possible while keeping
the discrepancy as low as
possible.
Tools>Options
Options to provide a
comprehensive goodness of fit
report of a standard response
surface to determine the
adequacy to be applied in Goal
Driven Optimization (GDO),
Design for Six Sigma (DFSS), and
Robust Design (RD) samples.
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Design points
Training Manual
Central Composite Design
For deterministic method the locations of the automatic design points are
determine according to a DOE method that is by default CCD with fractional
factorial design.
•If N is the number of input parameters then CCD consists of:
1. One center point.
2. 2*N axis point located at -a and +a position on each axis of the selected input
parameter.
3. 2^(N-f) factorial points located at the -1 and +1 position along the diagonals
of the input parameter space.
Number of Automatic design
points as a function of the
number of input parameters
Optimal Space-Filling Design
•You can create optimal space filling Design of Experiment (DOE) plans
according to specified criteria. The option allows you to fill the design space
efficiently with as few number of points as possible.
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Design points
Training Manual
• For the deterministic method, the locations of the generated design
points are determined according to a design of experiments method
that is by default a central composite design with a fractional factorial
design. Response Surfaces will be generated from the associated
DOE solution.
• Generated design points are temporarily written to the Table of
Design Points in the project's Parameter Set Bar during the solution.
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Meta Model Type
Training Manual
Standard Response Surface – Full 2nd Order Polynomials (default)
Assumes n sampling points and for each sampling point the
corresponding values of the response parameters are known.
Determines the relationship between the input and output parameters and
the response parameters based on these sampling points.
The resulting approximation of the output parameters as a function of
input parameters is called response surface.
Second order polynomial is preferred.
Kriging
Comprehensive goodness of the fit of standard response surface
Y(x)=f(x)+Z(x).
Combination of polynomial model plus Z(x)-realization of a normally
distributed Gaussian random process with mean zero, variance s^2 and
non zero variance.
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Meta Model Type
Training Manual
Non Parametric Regression
Support vector method.
Used hyperplane to categorize a subset of the input sample vectors
which are deemed sufficient to represent the output in question.
Neutral Network
Mathematical technique is based on the natural network in the human
brain.
Weighted functions are issued from the algorithm which minimizes the
distance between the interpolation and the known values (design points)
– learning process.
The error is check in every iteration.
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Goal Driven Optimization
Training Manual
• Preserve Design Points After DX Run in the cell's Properties view, also this
can be set up in Tools>Options>Design Exploration dialog.
• Preserving design points has the advantage of letting the user see the exact
design points used by a Design Exploration cell. In a Parameters Correlation
analysis for instance, it is the only way to see the actual design points used
to calculate the correlation since there is no preview for that cell.
• Preserving design points will allow subsequent Design Exploration update to
reuse the existing Design Points, which will reduce the CPU cost.
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Goal Driven Optimization
Training Manual
• The Screening approach is a non-iterative direct sampling method by
a quasi-random number generator.
• The MOGA approach is an iterative Multi-objective Genetic Algorithm,
which can optimize problems with continuous input parameters.
• NLPQL is a gradient based single objective optimizer which is based
on quasi-Newton methods.
• MOGA is better for calculating the global optima while NLPQL is a
gradient-based algorithm ideally suited for local optimization. So you
can start with Screening or MOGA to locate the multiple tentative
optima and then refine with NLPQL to zoom in on the individual local
maximum or minimum value. Problems with mixed parameter types
(i.e., usability, discrete, or scenario parameters with continuous
parameters) or discrete problems cannot currently be handled by the
MOGA or NLPQL techniques and in these cases you will only be able
to use the Screening technique.
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Goal Driven Optimization
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Training Manual
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Goal Driven Optimization
Training Manual
• The screen below shows various parametric preferences and their
importance specified on the Goals and Candidate Designs screen.
• Once goals are stated, a set of samples is generated from
which candidate designs will be obtained.
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Goal Driven Optimization
Training Manual
• At least one of the output parameters should have an Objective of
Maximize, Minimize, or Seek Target in order to do optimization with
the MOGA or NLPQL methods (only one output can have an
objective for the NLPQL method).
• If this is not done, then the optimization problem is either undefined
(No Objective) or is merely a constraint satisfaction problem
(Objective set to >= Target or <= Target or = Target . When the
problem is not defined, the MOGA or NLPQL analysis cannot be run.
• Screening method does not depend on any parameter settings and
can be used to perform preliminary design studies.
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Goal Driven Optimization
Training Manual
• After generating a sample set, candidate designs are generated and displayed
along with parameter rankings.
• Parameter values are displayed along with rankings corresponding to stated
goals.
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Goal Driven Optimization
Training Manual
• Another useful feature of GDO is the ability to visualize the impact
that variations in one parameter may have on another.
• These are called “Trade Off Plots” and are available in either 2D or
3D, depending on the number of parameters.
– Trade off plots use the concept of Pareto optimal points which are
discussed later.
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Goal Driven Optimization
Training Manual
• Regardless of how a candidate design is chosen (from response
charts or goal driven optimization), it can be stored for future study
as a design point.
• From Goal Driven Optimization:
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Creating a DX Scenario - example
Training Manual
• We will walk through the process of setting up a scenario in DX.
• In this example we’ll use DesignModeler as our geometry source.
There is no difference to the process when using a commercial CAD
system.
• We begin by specifying the geometry parameter(s) in our DM model.
Geometry parameters
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Creating a DX Scenario - example
Training Manual
Input and output parameters from Mechanical application.
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Creating a DX Scenario - example
Training Manual
• Once the analysis model is prepared, DX is started from the Project Schematic.
Double click on Goal
Driven Optimization in
the Design
Exploration Toolbox.
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Creating a DX Scenario - example
Training Manual
• At this point, only design points and parameter definitions and ranges
are specified.
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Creating a DX Scenario - example
Training Manual
We can Preview all design points at
this point, no outputs are calculated.
By updating design of
experiments, output
parameters are
calculated.
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Creating a DX Scenario - example
Training Manual
• From the views section,
“Responses” can be chosen to
review the calculated data.
• The results information can be
viewed in several formats listed
under Response Charts.
• The response charts allow potential
design decisions to be evaluated
visually.
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Creating a DX Scenario - example
Training Manual
• As seen, response charts are means of exploring the
entirety of a design and selecting desired design points
visually. Goal Driven Optimization is a continuation of
generating a response surface. Goal Driven Optimization is
a three step process:
– Generate samples
– Specify design goals
– Generate candidate designs
• Before generating candidates, DX must generate a group of
sample design points.
– The number of samples is user specified. The more
samples generated, the more refined the candidate
designs will be.
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Creating a DX Scenario - example
Training Manual
• The second step is specifying the design goals.
– The values as well as the importance of each input and response
parameter is set by clicking in the appropriate field.
• The third step is to generate candidate designs for evaluation.
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Creating a DX Scenario - example
Training Manual
• Three candidate designs are generated based on the stated goals.
• Once a suitable design is chosen it can be stored as a response design for
further study.
• Numerous candidate designs can be saved as response designs. Before
finalizing a design based on this data, a design point should be generated to
insure the validity of the choice.
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