Automatic machining feature recognition based on MBD and process semantics

Abstract

Automatic feature recognition (AFR) bridges CAD and CAPP systems. Existing AFR methods mainly consider feature geometries, and the recognized machining features have limited process attributes for process planning. With the model-based definition (MBD) becoming more advanced in the industry, AFR methods need to be improved and upgraded. This manuscript investigates a systematic AFR method for machining parts that converts the part MBD model to a structuralized feature model (SFM) based on process semantics. At its core, process semantics is the abstraction of machining objects and methods, which represents the machining requirements of machining position, machining type, machining accuracy and other process elements. Distinguished from existing AFR methods, the method in this study employs a part MBD model as the data source and provides a new definition of machining features from a process semantic perspective. Detailed data extraction and preprocessing methods of the MBD model are furnished. To identify feature geometries, a new classification methodology of machining features for defining the stable topology structure (SAF) and semi-stable topology structure (SSF) features is presented. An improved center-subgraph method is further proposed to incorporate hint-based and rule-based methods to enhance the performance of the traditional graph method. Furthermore, the generalized GAAG has been presented with an implementation method to address the completeness of graph representation and to reduce the computational effort of graph search. In addition, to extend the feature domain, the recognition methods for high-level composite features are discussed in detail. The feature attributes were solved based on the achieved feature subgraph, and the extraction methods were detailed according to process planning requirements. The feature recognition results can be directly applied for downstream machining process planning.