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.