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Research Papers

A Design for Additive Manufacturing Ontology to Support Manufacturability Analysis

[+] Author and Article Information
Samyeon Kim

Digital Manufacturing and Design Centre,
Singapore University of Technology and Design,
8 Somapah Road,
Singapore 487372, Singapore
e-mail: samyeon_kim@sutd.edu.sg

David W. Rosen

The George W. Woodruff School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332;
Digital Manufacturing and Design Centre,
Singapore University of Technology and Design,
8 Somapah Road,
Singapore 487372, Singapore
e-mails: david.rosen@me.gatech.edu; david_rosen@sutd.edu.sg

Paul Witherell

Systems Integration Division,
National Institute of Standards and Technology,
100 Bureau Drive,
Gaithersburg, MD 20899
e-mail: paul.witherell@nist.gov

Hyunwoong Ko

Systems Integration Division,
National Institute of Standards and Technology,
100 Bureau Drive,
Gaithersburg, MD 20899
e-mail: hyunwoong.ko@nist.gov

1Corresponding author.

2Present address: School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.

Manuscript received December 11, 2018; final manuscript received March 26, 2019; published online June 13, 2019. Assoc. Editor: Charlie C. L. Wang.

J. Comput. Inf. Sci. Eng 19(4), 041014 (Jun 13, 2019) (10 pages) Paper No: JCISE-18-1319; doi: 10.1115/1.4043531 History: Received December 11, 2018; Accepted March 27, 2019

Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure the 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. Furthermore, the wide variety of AM processes, materials, and machines creates challenges in determining manufacturability constraints. Therefore, this study presents a DFAM ontology using the web ontology language (OWL) to semantically model DFAM knowledge and retrieve that knowledge. The goal of the proposed DFAM ontology is to provide a structure for information on part design, AM processes, and AM capability to represent design rules. Furthermore, the manufacturing feature concept is introduced to indicate design features that are considerably constrained by given AM processes. After developing the DFAM ontology, queries based on design rules are represented to explicitly retrieve DFAM knowledge and analyze manufacturability using Semantic Query-enhanced Web Rule Language (SQWRL). The SQWRL rules enable effective reasoning to evaluate design features against manufacturing constraints. The usefulness of the DFAM ontology is demonstrated in a case study where design features of a bracket are selected as manufacturing features based on a rule development process. This study contributes to developing a reusable and upgradable knowledge base that can be used to perform manufacturing analysis.

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Figures

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Fig. 1

Overview of DFAM ontology structure

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Fig. 2

DFAM ontology hierarchy of (a) higher classes, (b) Feature, (c) Parameter, and (d) AM capability

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Fig. 3

Object properties between classes in the DFAM ontology

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Fig. 4

Overhang feature (a) based on downskin angle (θ) and normal vector (n⇀) of downskin area and (b) with support structure

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Fig. 5

A rule development process for manufacturability analysis and design recommendation

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Fig. 6

GE jet engine bracket: (a) conventional design and (b) optimized design for AM

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Fig. 7

Individual usage of an instance in the Protégé tool

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Fig. 8

A result of a query for clearance

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Fig. 9

A result of a query for downskin angle of the tilted wall

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Fig. 10

A result of a query for overhang features that require supports

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Fig. 11

Alternative hole cross sections: teardrop (a) and diamond (b)

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Fig. 12

A result of a query for hole cross-sectional recommendations

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