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Journal of Computing and Information Science in Engineering | Volume 7 | Issue 2 | Research Paper
RESEARCH PAPERS

Ontologies for Supporting Engineering Design Optimization

[+] Author and Article Information
Paul Witherell

Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003pww@student.umass.edu

Sundar Krishnamurty

Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003skrishna@ecs.umass.edu

Ian R. Grosse

Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003grosse@ecs.umass.edu

J. Comput. Inf. Sci. Eng 7(2), 141-150 (Nov 09, 2006) (10 pages) doi:10.1115/1.2720882 History: Received January 10, 2006; Revised November 09, 2006
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This paper presents an optimization ontology and its implementation into a prototype computational knowledge-based tool dubbed ONTOP (ontology for optimization). Salient feature of ONTOP include a knowledge base that incorporates both standardized optimization terminology, formal method definitions, and often unrecorded optimization details, such as any idealizations and assumptions that may be made when creating an optimization model, as well as the model developer’s rationale and justification behind these idealizations and assumptions. ONTOP was developed using Protégé, a Java-based, free open-source ontology development environment created by Stanford University. Two engineering design optimization case studies are presented. The first case study consists of the optimization of a structural beam element and demonstrates ONTOP ’s ability to address the variations in an optimal solution that may arise when different techniques and approaches are used. A second case study, a more complex design problem that deals with the optimization of an impeller of a pediatric left ventricular heart assist device, demonstrates the wealth of knowledge ONTOP is able to capture. Together, these test beds help illustrate the potential value of an ontology in representing application-specific knowledge while facilitating both the sharing and exchanging of this knowledge in engineering design optimization.
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+Flowchart of engineering design optimization process
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Flowchart of engineering design optimization process
Figure 2

Flowchart of engineering design optimization process

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+Slots used to define optimization model class when input in protégé
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Slots used to define optimization model class when input in protégé
Figure 3

Slots used to define optimization model class when input in protégé

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+Protégé ontology of optimization types
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Protégé ontology of optimization types
Figure 4

Protégé ontology of optimization types

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+Classic I-beam optimization problem, minimize volume by reducing cross-section
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Classic I-beam optimization problem, minimize volume by reducing cross-section
Figure 5

Classic I-beam optimization problem, minimize volume by reducing cross-section

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+I-beam optimization models in optimization ontology
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I-beam optimization models in optimization ontology
Figure 6

I-beam optimization models in optimization ontology

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+CAD model of PVAD impeller
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CAD model of PVAD impeller
Figure 7

CAD model of PVAD impeller

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+Diagram of optimization types
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Diagram of optimization types
Figure 1

Diagram of optimization types

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+Screenshot of application of optimization ontology (knowledge captured of CAD model to be optimized)
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Screenshot of application of optimization ontology (knowledge captured of CAD model to be optimized)
Figure 8

Screenshot of application of optimization ontology (knowledge captured of CAD model to be optimized)

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+Representation of multiple optimization models in ONTOP test bed, with one instance expanded
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Representation of multiple optimization models in ONTOP test bed, with one instance expanded
Figure 9

Representation of multiple optimization models in ONTOP test bed, with one instance expanded

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