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Parametric optimization is an important product design technique, especiallyin the context of the modern parametric feature-based CAD paradigm. Realizingits full potential, however, requires a closed loop between CAD and CAE (i.e.,CAD/CAE integration) with automatic design modifications and simulationupdates. Conventionally the approach of model conversion is often employed toform the loop, but this way of working is hard to automate and requires manualinputs. As a result, the overall optimization process is too laborious to beacceptable. To address this issue, a new method for parametric optimization isintroduced in this paper, based on a unified model representation scheme calledeXtended Voxels (XVoxels). This scheme hybridizes feature models and voxelmodels into a new concept of semantic voxels, where the voxel part isresponsible for FEM solving, and the semantic part is responsible forhigh-level information to capture both design and simulation intents. As such,it can establish a direct mapping between design models and analysis models,which in turn enables automatic updates on simulation results for designmodifications, and vice versa -- effectively a closed loop between CAD and CAE.In addition, robust and efficient geometric algorithms for manipulating XVoxelmodels and efficient numerical methods (based on the recent finite cell method)for simulating XVoxel models are provided. The presented method has beenvalidated by a series of case studies of increasing complexity to demonstrateits effectiveness. In particular, a computational efficiency improvement of upto 55.8 times the existing FCM method has been seen.

XVoxel-Based Parametric Design Optimization of Feature Models