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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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References

1
Weihe, K., 2002, “Optimization Algorithms—Basic Terminology,” edited by T.U.Darmstadt, Germany, www.algo.informatik.tu-darmstadt.de/lehre/2002ss/optalgo/grundlagen.ps
2
Messac, A., and Chen, W., 2000, “The Engineering Design Discipline: Is Its Confounding Lexicon Hindering its Evolution?” Engineering Evaluation and Cost Analysis, special edition on “Decision-Based Design: Status & Promise,” 3 (1-2), pp. 67–88.
3
Vanderplaats, G. N., 2001, "Numerical Optimization Techniques for Engineering Design", 3rd ed., Vanderplaats Research & Development.
4
The Mathworks, Inc., http://www.mathworks.com/
5
Mathsoft Engineering & Education, Inc., Mathcad, http://www.mathcad.com/
6
Fourer, R., Gay, D. M., and Kernighan, B. W., 2002, "AMPL-A Modeling Language for Mathematical Programming", Duxbury Press, Brooks/Cole Publishing, Belmont, MA.
7
Fourer, R., Gay, D. M., and Kernighan, B. W., 1990, “A Modeling Language for Mathematical Programming,” Manage. Sci., 36 , 519–554.
8
ANSYS, Inc., www.ansys.com
9
Parametric Technology Inc., Pro/ENGINEER, http://www.ptc.com/products/proe/wildfire/
10
Engineous Software, Inc., FIPER, http://www.engineous.com/product_FIPER.htm
11
Delen, D., Benjamin, P., and Erraguntla, M., 1998, “Integrated Modeling and Analysis Generator Environment (IMAGE): A Decision Support Tool,” Winter Simulation Conference .
12
American Association for Artificial Intelligence, http://www.aaai.org/
13
Portblue Corp., http://www.portblue.com/pub/index
14
Ferguson, J., and Goldie, S., 2000, “Applied Artificial Intelligence and the Management of Knowledge,” Bristol Bus. Sch. Teach. Res. Rev., 3 .
15
Gincel, R., 2004, “Refining Enterprise Search,” Infoworld, October, pp. 1–3.
16
Gruber, T. R., 1993, “A Translation Approach to Portable Ontologies,” Knowl. Acquis., 5 (2) pp. 199–220.
17
Ahmed, S., Kim, S., and Wallace, K., 2005, “A Methodology for Creating Ontologies for Engineering Design,” ASME IDETC/CIE .
18
Webster's Seventh New Collegiate Dictionary, C., 1972, G.&C. Merriam-Webster, Inc., Springfield, MA, p. 590.
19
Musen, M. A., Fergerson, R. W., Grosso, W. E., Noy, N. F., Crubezy, M., and Gennari, J. H., 2000, “Component-Based Support for Building Knowledge-Acquisition Systems,” Conference on Intelligent Information Processing (IIP 2000) of the International Federation for Information Processing World Computer Congress (WCC 2000) , Beijing.
20
Grosse, I. R., Milton-Benoit, J. M., and Wileden, J., 2005, “Ontologies for Supporting Engineering Analysis Models,” Artif. Intell. Eng. Des. Anal. Manuf.  [CrossRef], 19 (1), pp. 1–18.
21
Witherell, P. W., Grosse, I. R., and Krishnamurty, S., 2005, “Ontologies and Tools for Supporting Decision-Based Design,” International Conference for E-Design , Orlando.
22
Bajaj, M., Paredis, C. J., Rathnam, T., and Peak, R., 2005, “Federated Product Models for Enabling Simulation-Based Product Lifecycle Management,” 2005 ASME International Mechanical Engineering Congress and Exposition , Orlando.
23
Liang, V. C., and Paredis, C. J., 2004, “A Port Ontology for Conceptual Design of Systems,” ASME J. Comput. Inf. Sci. Eng.  [CrossRef], 4 (3), pp. 206–217.
24
Ciocoiu, M., Nau, D., and Gruninger, M., 2001, “Onotlogies for Integrating Engineering Applications,” ASME J. Comput. Inf. Sci. Eng.  [CrossRef], 1 , pp. 12–22.
25
Dolan, E., Fourer, R., Goux, J., and Munson, T., 2002, “Kestrel: An Interface From Modeling Systems to the NEOS Server,” Technical Report, Mathematics and Computer Science Division, Argonne National Laboratory.
26
Schwabacher, M., Ellman, T., and Hirsh, H., 1998, “Learning to Set Up Numerical Optimizations of Engineering Designs,” Artif. Intell. Eng. Des. Anal. Manuf., 12 (2), pp. 173–192.
27
Protégé, 2003, http://www.protege.stanford.edu, Stanford University.
28
Gennari, J., Musen, M. A., Fergerson, R. W., Grosso, W. E., Crubézy, M., Eriksson, H., Noy, N. F., and Tu, S. W., 2002, “The Evolution of Protégé: An Environment for Knowledge-Based Systems Development,” Int. J. Hum.-Comput. Stud., 58 (1), pp. 89–123.
29
Noy, N. F., Sintek, M., Decker, S., Crubezy, M., Fergerson, R. W., and Musen, M. A., 2001, “Creating Semantic Web Contents With Protege-2000,” IEEE Intell. Syst.  [CrossRef], 16 (2), 60–71.
30
Gruber, T., and Olsen, G., 1994, “An Ontology for Engineering Mathematics,” "Proc. of 4th International Conference on Principles of Knowledge Representation and Reasoning", Morgan Kaufmann, San Mateo, CA, Doyle, J., TorassoP., and Sandewall, E., eds., pp. 258–269..
31
Gruber, T. R., 1993, “Toward Principles for the Design of Ontologies Used for Knowledge Sharing,” Padua Workshop on Formal Ontology , March.
32
World Wide Web Consortium (W3C), http://www.w3.org/2004/OWL/
33
World Wide Web Consortium (W3C), http://www.w3.org/XML/
34
Lin, J., Fox, M. S., and Bilgic, T., 1996, “A Requirement Ontology for Engineering Design,” Concurr. Eng. Res. Appl.  [CrossRef], 4 (3), pp. 279–291.

Figures

Grahic Jump Location
+Flowchart of engineering design optimization process
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Flowchart of engineering design optimization process
Figure 2

Flowchart of engineering design optimization process

Grahic Jump Location
+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

Grahic Jump Location
+Protégé ontology of optimization types
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Protégé ontology of optimization types
Figure 4

Protégé ontology of optimization types

Grahic Jump Location
+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

Grahic Jump Location
+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

Grahic Jump Location
+CAD model of PVAD impeller
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CAD model of PVAD impeller
Figure 7

CAD model of PVAD impeller

Grahic Jump Location
+Diagram of optimization types
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Diagram of optimization types
Figure 1

Diagram of optimization types

Grahic Jump Location
+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)

Grahic Jump Location
+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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