Shape optimization has become an important tool in industry to minimize weight and generate new designs. At the same time, companies are turning to CAD-centric design strategies where robust parametric CAD models are used to generate new designs and part-families of current designs, as well as the tooling and manufacturing procedures. However, due to its complexity, the optimal topology results are often discarded or recreated by hand into a CAD model. From a design stand point, the results can be improved with the use of manufacturing constraints on the shape optimization process. These constraints improve the manufacturability based on common manufacturing practices. Even with these improvements, the process of converting topology results to CAD can cost substantial amounts of time and money. This thesis proposes a method of semi-automatically recognizing the voids, created during the shape optimization process, with parametric features based on CAD geometry construction. These parametric features are based on sets of cross-sectional shapes and spine rules to create solid objects. These features are then sent to the CAD part file via programming APIs that exist in the software packages. By recognizing features usable to the CAD systems, the voids can be characterized in the CAD model using robust dimensional constraints. This allows for the CAD model approximation to represent the topology optimization results with dimensional values from simpler shapes. Size optimization can then be applied to optimize the approximating model and regain any fidelity loss in the analytic model. Test cases created with and without manufacturing constraints show considerable promise in a proof-of-concept scenario. These tests utilize the topology optimization software HyperMesh from Altair and the CAD package NX 4.0 from Siemens (formerly UGS). The voids from shape optimization in these tests are recognized inside of HyperMesh, fit with a simple parametric feature, and created in the part model using the Open C API in NX.