Ontology based automatic feature recognition framework

•We build a novel ontology-based framework for automatic feature recognition on STEP-based solid models.•The machining features are hierarchically captured in an ontology which makes it easier for extension, customization and maintenance.•Semantic rules and formal logical expressions are used to define the features explicitly.•STEP files are interpreted as Ontology facts through a STEP parser.•A backward chained ontology reasoner is applied to extract features from interpreted STEP files.

AFR has long been realized as a key technology for design automation. A significant shortcoming in AFR is that most of them are individual systems that are isolated from each other, due to the absence of a standard feature library or feature modeling techniques. Few studies attempted to overcome this problem by allowing a certain degree of user customization or extension, which are still far from success. In order to address this issue, this paper proposes an ontology-based feature recognition framework. In the framework, features are captured transparently and hierarchically within a formal OWL ontology, and the feature recognition is achieved by applying an efficient backward-chained ontology reasoner to reason through the ontology. The resulting feature recognition system shows a high level of flexibility, maintainability, and explainability, for both representing and recognizing features. The effectiveness of the framework is finally demonstrated with three case studies.