Research Papers

A Design for Additive Manufacturing Ontology

[+] Author and Article Information
Mahmoud Dinar

G. W. Woodruff School of
Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: mdinar3@gatech.edu

David W. Rosen

G. W. Woodruff School of
Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received August 31, 2016; final manuscript received January 10, 2017; published online February 16, 2017. Assoc. Editor: Yong Chen.

J. Comput. Inf. Sci. Eng 17(2), 021013 (Feb 16, 2017) (9 pages) Paper No: JCISE-16-2065; doi: 10.1115/1.4035787 History: Received August 31, 2016; Revised January 10, 2017

Design for additive manufacturing (DFAM) gives designers new freedoms to create complex geometries and combine parts into one. However, it has its own limitations, and more importantly, requires a shift in thinking from traditional design for subtractive manufacturing. There is a lack of formal and structured guidelines, especially for novice designers. To formalize knowledge of DFAM, we have developed an ontology using formal web ontology language (OWL)/resource description framework (RDF) representations in the Protégé tool. The description logic formalism facilitates expressing domain knowledge as well as capturing information from benchmark studies. This is demonstrated in a case study with three design features: revolute joint, threaded assembly (screw connection), and slider–crank. How multiple instances (build events) are stored and retrieved in the knowledge base is discussed in light of modeling requirements for the DFAM knowledge base: knowledge capture and reuse, supporting a tutoring system, integration into cad tools. A set of competency questions are described to evaluate knowledge retrieval. Examples are given with SPARQL queries. Reasoning with semantic web rule language (SWRL) is exemplified for manufacturability analysis. Knowledge documentation is the main objective of the current ontology. However, description logic creates multiple opportunities for future work, including representing and reasoning about DFAM rules in a structured modular hierarchy, discovering new rules with induction, and recognizing patterns with classification, e.g., what leads to “successful” versus “unsuccessful” fabrications.

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Grahic Jump Location
Fig. 1

The hierarchical entity structure of the DFAM ontology

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Fig. 2

Sample models and fabricated design features

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Fig. 3

Network graph of the revolute joint entities and instances

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Fig. 4

Showing usage of all entities and attributes of an instance in the Protégé tool

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Fig. 5

Storing parametric data with assertions

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Fig. 6

SPARQL query for a competency question

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Fig. 7

Example of modular reasoning rules with Prolog




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