ANSYS Tutorial: Design Optimization in DX ANSYS Workbench Tutorial: Want to know more about you model? Run a What if scenario? How sensitive are you results for changes in the input variables? Maybe optimize your model in terms of weight? Then DesignXplorer is the tool for you.

This article will first give you a short summation of what DesignXplorer is and then show how to set up a simple optimization.

What is DesignXplorer?

Design Explorer is based on a method called Design of Experiments (DOE). This together with various optimization methods helps the program to develop an optimized structure based on selected input and output parameters. Input parameters can either come from DesignModeler or from various CAD systems. These parameters can be in terms of thickness, length, etc. They can also come from Mechanical in terms of forces, material properties, etc. The output parameters are calculated in Mechanical and can for example be in terms of total mass, stress or frequency response. After setting up an analysis with a number of input parameters and out parameters there are the steps that can be run within DesignXplorer:

Design of Experiments Response surface Optimization Six Sigma Analysis

Design of Experiments

Design of experiments is the foundation that everything within DesignXplorer is built on. What this technique is about is to determine how many and for what input values the analysis shall be run. There are various techniques for this but the same goal for all is to get as good response surface as possible with as few input combinations as possible. So basically this step defines is how many analysis that will be run. Each combination that ANSYS solves for is referred to as a Design Points.

Response Surface

When the Design of Experiments is run the next step is to create a response surface based on these results. A response surface will be created for each output parameter. The response surface is basically created via curve fit through the Design Points. From this response surface you can then investigate output results for input variable combinations that hasn´t been solved for.

Goal Driven Optimization

To help you to select the combination of input variables that satisfies your goals best, you can run a Goal Driven Optimization. Here you have the possibility to give all you parameters different objective functions on which you also can give different importance. ANSYS will then give you a number of candidates that satisfies your goals in the best way.

Six Sigma Analysis

The Six Sigma Analysis is a method that includes both a Design of Experiment and a Response Analysis. What resides is that you also have the possibility to set the input parameters as uncertainties parameters. This means that you can see how uncertainties in the input variables will affect the results for the output parameters.

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