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Ontologies for Supporting Engineering Design Optimization Paul Witherell National Institute of Standards and Technology, Paul.Witherell@Nist.Gov Center for e-Design Publications Center for e-Design 6-2007 Ontologies for Supporting Engineering Design Optimization Paul Witherell National Institute of Standards and Technology, [email protected] Sundar Krishnamurty University of Massachusetts Amherst, [email protected] Ian R. Grosse University of Massachusetts Amherst, [email protected] Follow this and additional works at: http://lib.dr.iastate.edu/edesign_pubs Part of the Computer-Aided Engineering and Design Commons, and the Industrial Engineering Commons Recommended Citation Witherell, Paul; Krishnamurty, Sundar; and Grosse, Ian R., "Ontologies for Supporting Engineering Design Optimization" (2007). Center for e-Design Publications. 18. http://lib.dr.iastate.edu/edesign_pubs/18 This Article is brought to you for free and open access by the Center for e-Design at Iowa State University Digital Repository. It has been accepted for inclusion in Center for e-Design Publications by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Ontologies for Supporting Engineering Design Optimization Abstract 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 ev ntricular 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. Keywords ontology, optimization, engineering design optimization Disciplines Computer-Aided Engineering and Design | Industrial Engineering Comments This article is from Journal of Computing and Information Science in Engineering 7 (2007): 141–150, doi:10.1115/1.2720882. Posted with permission. This article is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/edesign_pubs/18 Ontologies for Supporting Engineering Design Optimization This paper presents an optimization ontology and its implementation into a prototype Paul Witherell computational knowledge-based tool dubbed ONTOP (ontology for optimization). Salient e-mail: [email protected] feature of ONTOP include a knowledge base that incorporates both standardized optimi- zation terminology, formal method definitions, and often unrecorded optimization details, Sundar Krishnamurty such as any idealizations and assumptions that may be made when creating an optimi- e-mail: [email protected] zation 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 Ian R. Grosse open-source ontology development environment created by Stanford University. Two en- e-mail: [email protected] gineering 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 ad- Department of Mechanical and Industrial dress the variations in an optimal solution that may arise when different techniques and Engineering, approaches are used. A second case study, a more complex design problem that deals University of Massachusetts, with the optimization of an impeller of a pediatric left ventricular heart assist device, Amherst, MA 01003 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 engi- neering design optimization. ͓DOI: 10.1115/1.2720882͔ Keywords: ontology, optimization, engineering design optimization Introduction optimization. The ontology will provide methods for both captur- ing and sharing this often neglected higher-level knowledge. Optimization, most noticeably design optimization, has estab- lished itself as a mainstay in the field of engineering, becoming an integral part of engineering design process. The application of the Optimization Lexicons, Methods, and Tools field of optimization to engineering design has grown to involve a One barrier consistently encountered when facilitating the shar- substantial number of terms, methods, and software programs. ing of knowledge within the field of optimization has become Consequently, an immense amount of knowledge is associated inconsistent terminology, or lexicons. Weihe ͓1͔ concedes the use with optimization within engineering. The organization and avail- of inconsistent terminology in optimization, noting “Unfortu- ability of this knowledge can play a significant role when inter- nately, the terminology found in the literature is not at all stan- preting outcomes of a design optimization problem. dardized. Therefore, if a piece of terminology is not underlined, Solutions to optimization problems, as with results obtained in this only means that a substantial number of textbooks and origi- many other fields of engineering, depend heavily on initial as- nal papers has adopted it and uses it in the same way as here.” A sumptions and conditions. Optimization results, especially in similar problem of inconsistent lexicons also exists within the complex problems such as nonlinear multivariate engineering engineering design community, as recognized by Messac and problems, can vary widely depending on the type of approach or Chen ͓2͔. The authors refer to the wide variety of lexicons as technique used when solving of a problem. A situation of multiple “practices that may hinder intelligible discourse within the engi- solutions is commonly encountered, as demonstrated later in the neering design literature.” As an example of vagueness in termi- paper when optimizing a relatively simple cantilever I-beam. A nology, Messac and Chen discuss the failure of engineering design reliable mechanism is needed for ensuring the integrity of a prob- lexicons to distinguish between the terms design metric and ob- lem, the optimization process, and the resulting optimized solu- jective function, noting multiple variations of the application of tions. This paper introduces the design and development of such this terminology. The authors then detail the current faults of the use of widespread lexicons within the design community. an approach, using the instantiation of an ontology to serve as a The widespread terminology used in both optimization and de- formal method for capturing and retaining domain-specific knowl- sign, as well as the large assortment of techniques that may be edge in an explicit, easy-to-follow, and comprehensive manner. ͑ ͒ used during design optimization, can easily lead to confusion and The ontology implementation ONTOP ontology for optimization poor knowledge sharing and distribution. The creation of formal, will address the critical information gap in the engineering design widely accepted definitions of design optimization lexicons would optimization process. This information gap includes the complica- prevent many miscommunications among the engineering com- tions created through inconsistent optimization lexicons, the large munity, providing a much needed consensual understanding of the number of optimization methods that have been created, and the terminology. Although we acknowledge that such a large under- lack of communication between existing optimization tools. taking will also require the consent and recognition of the com- A substantial, yet widely overlooked problem in engineering, munity, we believe a consensus can be achieved for specific sec- the absence of an ability to represent abstract design-related tions of the design optimization community. knowledge, will be one of the main focuses of the ontology for The amount of methods used in optimization has greatly in- creased during the maturation of the field of optimization ͓3͔. Through the development of new mathematical approaches on ͑ ͒ Contributed by the Engineering Informatics EIX Committee of ASME for pub- optimization, or slightly altering existing ones, new optimization lication in the JOURNAL OF COMPUTING INFORMATON SCIENCE AND ENGINEERING. Manu- script received January 10, 2006; final manuscript received November 9, 2006. As- methods are created. New methods require unique knowledge to soc. Editor: John Michopoulos. be successfully applied to an optimization problem. A diagram of Journal of Computing and Information Science in Engineering JUNE 2007, Vol. 7 / 141 Copyright © 2007 by ASME Downloaded 28
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