A Novel Hybrid Method for Intelligent Machining Feature Recognition in Manufacturing Systems - Huo, Yunliang; Li, Zhenlong; Zhang, Ming; Zhu, Zhendong; Liu, Junbo (International Journal of Industrial Engineering: Theory, Applications and Practice, 2025)
A matter-element and graph-based machining feature recognition method is proposed to address the recognition of complex machining features and provide corresponding tool adaptation interfaces. First, since complex features are formed through Boolean operations of basic features, an inclusion relationship necessarily exists between complex and basic features. Therefore, the matter-element model, which excels at representing inter-object relationships, is employed to describe complex features, basic features, and their relationships. Next, an Attributed Adjacency Graph (AAG)-based algorithm is introduced to decompose the entire part and derive AAGs of machining features. The corresponding Attribute Adjacency Matrix (AAM) for each machining feature is constructed based on geometric element coding rules to enable basic feature recognition. Furthermore, using shared surfaces and edges extracted from the STEP neutral file, the recognized basic features are systematically organized as matter-element structures to represent complex features. The critical dimensions of complex features are determined by comparing the geometric dimensions of the constituent basic features. Finally, a platform developed in Java 1.8 demonstrates the method’s practicality through a case study. Results indicate that the proposed method is not only straightforward to implement but also readily integrable with cutting processes.