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dc.contributor.advisorJoshi, Shiven_US
dc.contributor.authorUmaretiya, Jagdish R.
dc.creatorUmaretiya, Jagdish R.en_US
dc.date.accessioned2011-10-31T17:25:46Z
dc.date.available2011-10-31T17:25:46Z
dc.date.issued1990en_US
dc.identifier.urihttp://hdl.handle.net/10150/185035
dc.description.abstractThis report addresses the research applied towards the automation of the engineering design process, in particular the structural design process. The three important stages of the structural design process are: the specifications, preliminary design and the detailed design. An iterative redesign architecture of the structural design process lends itself to automation. The automation of the structural design can improve both the cost and the reliability, and enhance the productivity of the human designers. To the extent that the assumptions involved in the design process are explicitly represented and automatically inforced, the design errors resulting from the violated assumptions can be avoided. Artificial Intelligence (AI) addresses the automation of complex and knowledge-intensive tasks such as the structural design process. It involves the development of the Knowledge Based Expert System (KBES). There are several tools, also known as expert shells, and languages available for the development of knowledge-based expert systems. A general purpose language, called LISP, is very popular among researchers in AI and is used as an environmental tool for the development of the KBES for the structural design process. The resulting system, called Expert-SEISD, is very generic in nature. The Expert-SEISD is composed of the user interface, inference engine, domain specific knowledge and data bases and the knowledge acquisition. The present domain of the Expert-SEISD encompasses the design of structural components such as beams and plates. The knowledge acquisition module is developed to facilitate the incorporation of new capabilities (knowledge or data) for beams, plates and for new structural components. The decision making is an integral part of any design process. A decision-making model suitable for the specifications extraction and the preliminary design phases of the structural design process is proposed and developed based on the theory of fuzzy sets. The methods developed here are evaluated and compared with similar methods available in the literature. The new method, based on the union of fuzzy sets and contrast intensification, was found suitable for the proposed model. It was implemented as a separate module in the Expert-SEISD. A session with the Expert-SEISD is presented to demonstrate its capabilities of beam and plate designs and knowledge acquisition.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectEngineering design -- Data processingen_US
dc.subjectEngineering, Aerospaceen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer-aided engineeringen_US
dc.subjectExpert systems (Computer science)en_US
dc.titleSpecifications extraction and synthesis: Their correlations with preliminary design.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.identifier.oclc704423987en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberKamel, H.en_US
dc.contributor.committeememberChandra, A.en_US
dc.contributor.committeememberSimon, B.R.en_US
dc.contributor.committeememberRichard, R.M.en_US
dc.identifier.proquest9024654en_US
thesis.degree.disciplineAerospace and Mechanical Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
refterms.dateFOA2018-08-22T23:59:44Z
html.description.abstractThis report addresses the research applied towards the automation of the engineering design process, in particular the structural design process. The three important stages of the structural design process are: the specifications, preliminary design and the detailed design. An iterative redesign architecture of the structural design process lends itself to automation. The automation of the structural design can improve both the cost and the reliability, and enhance the productivity of the human designers. To the extent that the assumptions involved in the design process are explicitly represented and automatically inforced, the design errors resulting from the violated assumptions can be avoided. Artificial Intelligence (AI) addresses the automation of complex and knowledge-intensive tasks such as the structural design process. It involves the development of the Knowledge Based Expert System (KBES). There are several tools, also known as expert shells, and languages available for the development of knowledge-based expert systems. A general purpose language, called LISP, is very popular among researchers in AI and is used as an environmental tool for the development of the KBES for the structural design process. The resulting system, called Expert-SEISD, is very generic in nature. The Expert-SEISD is composed of the user interface, inference engine, domain specific knowledge and data bases and the knowledge acquisition. The present domain of the Expert-SEISD encompasses the design of structural components such as beams and plates. The knowledge acquisition module is developed to facilitate the incorporation of new capabilities (knowledge or data) for beams, plates and for new structural components. The decision making is an integral part of any design process. A decision-making model suitable for the specifications extraction and the preliminary design phases of the structural design process is proposed and developed based on the theory of fuzzy sets. The methods developed here are evaluated and compared with similar methods available in the literature. The new method, based on the union of fuzzy sets and contrast intensification, was found suitable for the proposed model. It was implemented as a separate module in the Expert-SEISD. A session with the Expert-SEISD is presented to demonstrate its capabilities of beam and plate designs and knowledge acquisition.


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