Automatic machining feature recognition from STEP files

Abstract

Automatic machining feature recognition (AMFR) is a critical component of CAD/CAPP/CAM integration. Multiple intersecting feature intersection causes a major problem in the research field. Due to this issue, an automated machining feature recognition method is presented to overcome this issue. This research aims to group the data of symmetrical faces and efficiently sort the faces according to their Cartesian values. The machining feature (MF) recognition algorithm can differentiate between hole segments of toroidal, prismatic, cylindrical and conical with varied groove blinds and feature attributes. Four distinct case studies were conducted in this research, which consist of geometrical and topological feature data extraction from the part, sorting of faces throughout the part and recognition of holes and groove blinds. The feature extraction and recognition techniques are implemented using Python language for rotatable components to detect rotatable parts with the prismatic shape of holes and groove blinds by employing the STEP file; this is often assessed using different case studies, respectively.