One of the key unsolved problems in parametric solid modeling is a robust update (regeneration) of the solid's boundary representation, given a specified change in solid's parameter values. The fundamental difficulty lies in determining the mapping between boundary representations for solids in the same parametric family, also known as "persistent naming." We formulate the notion of Boundary Representation (BR-)variance for solids in the same parametric family based on the assumption of continuity: small changes in solid's parameter values should result in small changes in solid's boundary rep reeentation, which may include local collapses of cells in boundary representations. We show that our formulation gives the necessary conditions that must be satisfied by any BR-variant mappings between boundary representations. These conditions are powerful enough to identify invalid updates in many practical situations and provide a formal criteria for the recently proposed heuristic approaches to the "persistent naming" problem.1 Motivation
Ambiguity of parametric solid modelingFrom its very inception solid modeling has been synonymous with unambiguous (informationally complete) representations of homogeneously n-dimensional subsets of Euclidean space PO,9,8]. On the other hand, the recent rise of solid modeling as a principal information medium first in engineering and now in con-"Complete address: 1513 University Avenue, Madison, sumer applications probably has to do more with development and successful marketing of new parametric ("feature-based" and "constraint-based" ) user interfaces than with the mathematical soundness of solid modeling systems. Th@e parametric interfaces allow user to define and modify solid models in terms of highlevel parametric definitions that are constructed to have intuitive and appealing meaning to the user and/or ap plication (see [11] for examples and references). The success of the parametric solid modeling came at a high price: the new solid modeling systems no longer guarantee that the parametric models are valid or unambiguous, and the results of modeling operations are not always predictable.