Semantic Tagging Framework for Contextually Augmented Features

The value of (semi-)automatically recognizing the meaningful regions on a Computer Aided Design (CAD) model is well documented and has been traditionally studied as Geometric feature recognition. However, the effective tagging of the regions, especially tagging with contextually meaningful (i.e. semantic) keywords, has been elusive. In this paper, we focus on the non-geometric aspects of the tagging problem and present a semantic tagging framework. Specifically, given the contextually augmented instances created from the unidentified regions on a CAD model, the framework tags the instances with semantic keywords defined in the semantic model. This will provide the direct link between a CAD model and semantic models, and thus allow high-level reasoning to support design and manufacturing tasks. The feasibility of the approach has been verified by applying the developed framework to tag the regions on turbine blade models. This paper concludes with the future challenges of the approach.