Abstract
The elements of Computer-Aided Manufacturing, do not make full use of the part description stored in a CAD model because it exists in terms of low-level faces, edges and vertices or primitive volumes related to the manufacturing planning task. Consequently manufacturing planning still depends upon human expertise and input to interpret the part definition according to manufacturing needs.
Feature-based technology is becoming an important tool to resolve this and other related problems. One approach is to design the part using Features directly. Another approach is Manufacturing Feature Extraction and Recognition. Manufacturing Feature Extraction consists of searching for the part description, recognizing cavity features, extracting those features as solid volumes of material to be removed. Feature Recognition involves raising this information to the level of part features which can be read by a process planning program. The feature extraction can be called optimal if the manufacturing cost of the component using those features can be minimized. An optimized feature extraction technique using two powerful optimization methods viz., Simulated Annealing and Genetic Algorithm is presented in this paper. This work has relevance in the areas of CAD/CAM linking, process planning and manufacturability assessment.
