Incremental boundary evaluation using inference of edge classifications

摘要

An incremental boundary-evaluation algorithm that exploits adjacency information in B-reps to minimize the number of explicit edge classifications required is presented. Evaluations of the implemented algorithm show that it performs reliably and well, although global optimization schemes could increase performance significantly. The steps of the algorithm, which include self-edge partitioning, cross-edge self-edge (CESE) classification, inference of self-edge classifications, and checking for split and merged shells, are discussed.