0
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
Article
References
Figures
Tables
Citing Articles

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.
FIGURES IN THIS ARTICLE
<>
Copyright © 2007 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

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
+Representation of multiple optimization models in ONTOP test bed, with one instance expanded
View Large  |  Save Figure  |  Download Slide (.ppt)
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

Grahic Jump Location
+Diagram of optimization types
View Large  |  Save Figure  |  Download Slide (.ppt)
Diagram of optimization types
Figure 1

Diagram of optimization types

Grahic Jump Location
+I-beam optimization models in optimization ontology
View Large  |  Save Figure  |  Download Slide (.ppt)
I-beam optimization models in optimization ontology
Figure 6

I-beam optimization models in optimization ontology

Grahic Jump Location
+CAD model of PVAD impeller
View Large  |  Save Figure  |  Download Slide (.ppt)
CAD model of PVAD impeller
Figure 7

CAD model of PVAD impeller

Grahic Jump Location
+Screenshot of application of optimization ontology (knowledge captured of CAD model to be optimized)
View Large  |  Save Figure  |  Download Slide (.ppt)
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
+Flowchart of engineering design optimization process
View Large  |  Save Figure  |  Download Slide (.ppt)
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
View Large  |  Save Figure  |  Download Slide (.ppt)
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
View Large  |  Save Figure  |  Download Slide (.ppt)
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
View Large  |  Save Figure  |  Download Slide (.ppt)
Classic I-beam optimization problem, minimize volume by reducing cross-section
Figure 5

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

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
A Methodology for Creating Ontologies for Engineering Design
J. Comput. Inf. Sci. Eng (June, 2007)
Editorial
J. Comput. Inf. Sci. Eng (December, 2007)
Extracting the Structure of Design Information From Collaborative Tagging
J. Comput. Inf. Sci. Eng (December, 2011)
A Port Ontology for Conceptual Design of Systems
J. Comput. Inf. Sci. Eng (September, 2004)
An Approach to Capturing Structure, Behavior, and Function of Artifacts in Computer-Aided Design
J. Comput. Inf. Sci. Eng (June, 2001)
Related eBook Content
Multiobjective Decision-Making Using Physical Programming
Decision Making in Engineering Design> Chapter 15
Free Search and Differential Evolution towards Dimensions Number Change
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16
Modified Bacterial Foraging Optimization for Engineering Design
Intelligent Engineering Systems through Artificial Neural Networks
Optimization Design for 3D Roadway Based on Space Analysis of GIS
Geological Engineering: Proceedings of the 1st International Conference (ICGE 2007)
An Easy-to-Approach Comprehensive Model and Computation for SOFC Performance and Design Optimization
Inaugural US-EU-China Thermophysics Conference-Renewable Energy 2009 (UECTC 2009 Proceedings)
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
This site uses cookies. By continuing to use our website, you are agreeing to our privacy policy. | Accept
Sign In