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Ontologies for supporting engineering analysis models

Published online by Cambridge University Press:  22 July 2005

IAN R. GROSSE ,
JOHN M. MILTON–BENOIT  and
JACK C. WILEDEN
IAN R. GROSSE
Affiliation:
Department of Mechanical and Industrial Engineering, 160 Governor's Drive, University of Massachusetts, Amherst, Massachusetts 01003-2210, USA
JOHN M. MILTON–BENOIT
Affiliation:
United Technologies Research Center, East Hartford, Connecticut, USA
JACK C. WILEDEN
Affiliation:
Department of Computer Science, University of Massachusetts, Amherst, Massachusetts 01003-2210, USA
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Abstract

In this paper we lay the foundations for exchanging, adapting, and interoperating engineering analysis models (EAMs). Our primary foundation is based upon the concept that engineering analysis models are knowledge-based abstractions of physical systems, and therefore knowledge sharing is the key to exchanging, adapting, and interoperating EAMs within or across organizations. To enable robust knowledge sharing, we propose a formal set of ontologies for classifying analysis modeling knowledge. To this end, the fundamental concepts that form the basis of all engineering analysis models are identified, described, and typed for implementation into a computational environment. This generic engineering analysis modeling ontology is extended to include distinct analysis subclasses. We discuss extension of the generic engineering analysis modeling class for two common analysis subclasses: continuum-based finite element models and lumped parameter or discrete analysis models. To illustrate how formal ontologies of engineering analysis modeling knowledge might facilitate knowledge exchange and improve reuse, adaptability, and interoperability of analysis models, we have developed a prototype engineering analysis modeling knowledge base, called ON-TEAM, based on our proposed ontologies. An industrial application is used to instantiate the ON-TEAM knowledge base and illustrate how such a system might improve the ability of organizations to efficiently exchange, adapt, and interoperate analysis models within a computer-based engineering environment. We have chosen Java as our implementation language for ON-TEAM so that we can fully exploit object-oriented technology, such as object inspection and the use of metaclasses and metaobjects, to operate on the knowledge base to perform a variety of tasks, such as knowledge inspection, editing, maintenance, model diagnosis, customized report generation of analysis models, model selection, automated customization of the knowledge interface based on the user expertise level, and interoperability assessment of distinct analysis models.

Keywords

Engineering Analysis ModelsInteroperabilityKnowledge BaseOntologies
Type
Research Article
Information
AI EDAM , Volume 19 , Issue 1 , February 2005 , pp. 1 - 18
Copyright
2005 Cambridge University Press

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