• DesignXplorer is a powerful approach to explore, understand and optimize your
engineering challenges.– Determine the key parameters influencing the design– Explore and understand the performance at other design or operating conditions– Find the conditions which give the best performance– Explore the robustness of the design
Design of Experiments• Specify the DOE Type• Specify each parameter range and type(Continuous, Discrete, Manufacturable Values)• Design Points are automatically chosen to explore the parametric space efficiently
• In a DOE study, the amount of design points increases quickly as the number of input parameters increases, which can reduce the efficiency of the analysis process.
• It is recommended to exclude unimportant input parameters from the DOE sampling in order to reduce unnecessary sampling points
• The parameter correlation tool allows us to identify important parameters
• Correlation matrices, determination matrices, correlation scatter plots, and sensitivity charts also help to understand the parametric relationships.
• Useful when there are many input parameters
DOE purpose and schemes
The purpose of a Design of Experiments is to gather a representative set of data to compute a Response Surface, and then run an Optimization (for a Response Surface Optimization).
Basically, a set of Design Points will be calculated.
The Response Surface accuracy will greatly depend on the DOE scheme, and especially the number of Design Points that were calculated.
DesignXplorer proposes several DOE schemes. Design Points are automatically chosen to explore the parametric space efficiently.
• Response Surface = Surrogate Model = Meta Model = Approximation Model
• Response Surfaces are functions of different nature where the output parameters are described in terms of the input parameters
• Response Surfaces provide the approximated values of the output parameters, everywhere in the analyzed design space, without the need to perform a complete solution
• The response surface methods described here are suitable for problems using up to ~10-15 input paramete
DesignXplorer proposes two Optimization workflows:
• Response Surface Optimization (RSO)
• Based on a DOE + Response Surface
• Computation time was spent at DOE step
• Changing Optimization criteria and re-runningis almost costless
• Optimum results are approximated and should be verified by an actual resolution
• Direct Optimization
• Straight forward
• Based on “real” solves
• Changing Optimization criteria and re-running is expensive
• Optimum results rely on an actual resolution
Six Sigma Analysis
• Typical analyses assume a fixed value for each input quantity and assigns a safety factor to account for these assumptions (deterministic)
• Design For Six Sigma provides a mechanism to include and account for scatter in input and provide insight into how they affect the system response (probabilistic)
• A product has Six Sigma quality if only 3.4 parts out of every 1 million manufactured fail
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