Super-Parametrizing CAD Models for Efficient Shape Optimization via Parametric Model Embedding

Methodologies for reducing the design-space dimensionality in simulation-driven design optimization have been recently developed based on unsupervised machine learning methods. These methods provide reduced dimensionality representations capable of maintaining a certain degree of design variability. Nevertheless, they usually do not allow to use the original CAD parameterization, representing a limitation to their widespread use in the industrial field, where the design parameters often pertain to well-established parametric CAD models. This work presents how to embed the parametric-model original parameters in a reduced-dimensionality representation. The method, which takes advantage from the definition of a newly-introduced generalized feature space, is demonstrated to the reparameterization of a free-form deformation design space.