Local parameterization of freeform shapes using freeform feature recognition
Form feature modeling is a much used shape modeling technique that offers high-level control over a shape. When a feature-based interpretation of shape data is not available, e.g. when a shape is obtained by a laser range scanner or from a database of shapes, then the features must be reconstructed through feature recognition. Many methods for the recognition of machining features exist, but these methods cannot be used for freeform feature recognition, of which the complexity is much larger. In this paper, a new freeform feature recognition method is presented that is based on a new definition of the freeform feature concept. The method uses a three-step approach to feature recognition, in which first the global shape of a feature is matched to the target shape model. In a second step, this global shape is locally adapted to the target shape by adapting the definition of the feature. Finally, if the desired configuration of the feature has been determined, it can be used to reconstruct the targets shape. In the first two steps, an evolutionary approach is taken to maximizing the similarity between the feature and the target shape. Finally, the target shape is reconstructed to incorporate the recognized feature. An extensive application example is given and the method is validated by applying it to a large number of artificially created test cases.
parameterization freeform feature feature recognition evolutionary computation shape reuse