Design of Experiments (DOE) with HEEDS MDO
When it's important to predict design sensitivities, or to gain a clearer understanding of your design space, a HEEDS® MDO Design of Experiments (DOE) study is often the ideal approach. It allows you to extract a great deal of useful information quickly, with the least computational or experimental effort possible. In a HEEDS DOE study, your CAE model is automatically evaluated multiple times with the design variables set to different values in each one.
Easily predict design sensitivities
A HEEDS DOE study helps you identify, and focus on, the variables that affect your design the most. Variables that are not important can then be ignored or set to values that are most convenient or least costly. This allows you to control quality more effectively while lowering cost.
Clearly understand your design space
The results of a HEEDS DOE sampling process can be used to generate an approximate model of your system (often called a response surface model or RSM). These models are very convenient for
DOE Sampling Methods
- Visualizing your design space
- Examining the relationships among variables and their effects on key responses.
- Quickly evaluating different designs without performing additional expensive CAE evaluations or experiments.
HEEDS MDO offers a broad range of DOE sampling methods and post-processing features. Even if you are not an expert at DOE, HEEDS' unique DOE wizard can guide you through the definition of your problem, ensuring that you obtain the information you need.
- Full factorial designs (2-level and 3-level)
- Fractional factorial designs (2-level and 3-level)
- Taguchi orthogonal arrays
- Plackett-Burman designs
- Latin hypercube designs
- Central composite designs
- D-optimal designs
- Taguchi robust design arrays
- User-defined arrays
- User-defined response data