The objective of this research is to develop techniques for the automatic generation of sculptured surface models in a spatially constrained environment, subject to specified intrinsic shape properties. Spatial constraints will be characterized by a distance metric relating surface points to polyhedral models which represent either components in the vicinity of the surface, or the boundaries of regions that the surface must avoid. The shape of the surface will be governed by constraints based on intrinsic properties such as tangency and curvature, which will be derived from general characterizations of design intent. To simultaneously address the independent goals of global obstacle avoidance and local control of intrinsic shape properties, surface synthesis will be formulated as a combinatorial optimization problem to be solved via simulated annealing (SA). Preliminary experiments with this technique are very promising. Some of the research issues to be addressed include: synthesis of general non-uniform rational B-spline surfaces; derivation of analytical surface design constraints based on objective characterizations of manufacturability and functional requirements, and from subjective measures of surface aesthetics; optimal estimation of parameters associated with the SA algorithm for surface synthesis; problem formulation for multiple spatial obstacles; possible hybrid SA/classical optimization techniques; performance oriented variations of the SA algorithm; and, user interface design and implementation. Automated surfacing capabilities will facilitate simultaneous engineering and provide product designers with a rapid analytical prototyping facility. Given the wide spectrum of potential applications, this research may have a profound impact on the efficiency of the development process for many products.
