Abstract
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.
Keywords
- CAD Model Design
- Medical Machining Parts Analysis
- Knowledgebased systems
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Zhang, H.L., Jiang, W., Wu, H., Shu, L. (2012). A Novel Automated Recognition System Based on Medical Machining CAD Models. In: He, J., Liu, X., Krupinski, E.A., Xu, G. (eds) Health Information Science. HIS 2012. Lecture Notes in Computer Science, vol 7231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29361-0_7
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DOI: https://doi.org/10.1007/978-3-642-29361-0_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29360-3
Online ISBN: 978-3-642-29361-0
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