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A Design for Additive Manufacturing Ontology to Support Manufacturability Analysis

[+] Author Affiliations
Samyeon Kim, David W. Rosen

Singapore University of Technology and Design, Singapore, Singapore

Paul Witherell

National Institute of Standards and Technology, Gaithersburg, MD

Hyunwoong Ko

Nanyang Technological University, Singapore, Singapore

Paper No. DETC2018-85848, pp. V02AT03A036; 10 pages
doi:10.1115/DETC2018-85848
From:
  • ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 44th Design Automation Conference
  • Quebec City, Quebec, Canada, August 26–29, 2018
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5175-3
  • Copyright © 2018 by ASME

abstract

Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for achieving target goals, e.g., reducing build-time. Therefore, this study presents a DFAM ontology using the web ontology language (OWL) to formalize DFAM knowledge and support queries for retrieving that knowledge. The DFAM ontology has three high level classes to represent design rules specifically: feature, parameter, and AM capability. Furthermore, the manufacturing feature concept is defined to link part design to AM process parameters. Since manufacturing features contain information on feature constraints of AM processes, the DFAM ontology supports manufacturability analysis of design features by reasoning with Semantic Query-enhanced Web Rule Language (SQWRL). The SQWRL rules in this study also help retrieve design recommendations for improving manufacturability. A case study is performed to illustrate usefulness of the DFAM ontology and SQWRL rule application. This study contributes to developing a knowledge base that can be reusable and upgradable and to analyzing manufacturing analysis to provide feedback about part designs to designers.

Copyright © 2018 by ASME

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