Graph-based heuristics for recognition of machined features from a 3D solid model
Computer-Aided Design
Knowledge-Based Manufacturing Management: Applications of Artificial Intelligence to the Effective Management of Manufacturing Companies
Novel ANN-based feature recognition incorporating design by features
Computers in Industry
Utilizing knowledge based mechanisms in automated feature recognition processes
BI'11 Proceedings of the 2011 international conference on Brain informatics
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CAD/CAM software products can help boost productivity for machining medical parts. However, the process of evaluating and re-calculating CAD model design is basically carried out manually. The demand for automated CAD process systems has been rising. Automated feature recognition (AFR) systems can improve system efficiency and effectiveness for processing CAD models in manufacturing sectors, particularly for designing medical machining parts. However, existing AFR methods are unable to fulfill industrial requirements for extracting and recognizing domain components from CAD models efficiently. In this paper we suggest a knowledge-based AFR system that can efficiently identify domain components from CAD models. The AFR knowledgebase incorporates rule-based methods for identifying core components from CAD models. The process of defining the rules and fact base structure is one of the most critical issues in the AFR system design. There is no existing technology available for generating inference rules from the STEP model format. The AFR-based system has successfully solved the technical issues in both the inference process and STEP-based extraction process. The skeleton software has been successfully developed based on the modularized system framework. The skeleton software can effectively recognize the common domain specific components.