Abstract:

The automatic feature recognition technology for sheet metal parts can extract machining feature information from parametric models, providing a fundamental basis for subsequent unfolding and process planning. However, current feature recognition methods for sheet metal parts struggle to distinguish between bending features and welding features that share the same topology, and the large number of faces in sheet metal parts also limits the improvement of feature recognition efficiency. This paper proposes a graph-simplification-based rapid recognition for bending features and unfolding optimization method in sheet metal parts. The proposed method redefines the bending feature and constructs a bending feature tree to represent the relationships among different bending features, thereby enabling the distinction between bending and welding features with identical topological structures. To improve the efficiency of bending feature recognition, a graph simplification algorithm based on inner loops and thickness faces is introduced before the construction of the bending feature tree to simplify the extended attribute adjacency graph. For sheet metal parts containing welding features, the unfolding process is optimized based on the bending feature tree to obtain the unfolded model. Experimental results demonstrate that the proposed algorithm can effectively distinguish bending features from welding features with identical topology. For sheet metal parts whose extended attribute adjacency graphs contain loop structures, the proposed method can transform them into tree-structured bending feature trees, thereby enabling effective unfolding. And typical case show that the efficiency of bending feature recognition improves by up to 89% compared with that before graph simplification.

Key words:

sheet metal, feature recognition, bending feature, welding feature, unfold