Unsupervised extraction of design components for a 3D parts-based representation

During CAD development and any kind of design optimisation over years a huge amount of geometries accumulate in a design department. To organize and structure these designs with respect to reusability, a hierarchical set of components on different scalings is extracted by the designers. This hierarchy allows to compose designs from several parts and to adapt the composition to the current task. Nevertheless, this hierarchy is imposed by humans and relies on their experiences. In the present paper a computational method is proposed for an unsupervised extraction of design components from a large repository of geometries. Methods known from the field of object and pattern recognition in images are transferred to the 3D design space to detect relevant features of geometries. The non-negative matrix factorization algorithm (NMF) is extended and tuned to the given task for an autonomous detection of design components. The results of the NMF additionally provide an overview on the distribution of these components in the design repository. The extracted components sum up in a parts-based representation which serves as a base for manual or computational design development or optimisation respectively.