Advanced Feature Detection Algorithms for Incrementally Formed Sheet Metal Parts

The rapid and accurate manufacture of sheet metal parts using Single Point Incremental Forming (SPIF) requires an expert Computer-Aided Process Planning (CAPP) system that can take in the CAD model of a part, and generate an optimized tool path. This, in turn, requires compensating for the behaviour of different features occurring in incrementally formed sheet metal parts. While an extensive taxonomy of features relevant in SPIF has been previously reported, algorithms are needed to detect these features and compensate for their behaviour. This paper reports the development of feature detection algorithms in STL part specifications that take into account geometry, curvature, location, orientation and process parameters to detect 33 different features within an expert CAPP system for SPIF. Case studies are then presented to validate the proposed algorithms. The detected features can be joined together in a network of features that can be analysed to manufacture parts with improved accuracy.status: publishe