An object-oriented, knowledge-based, non-procedural approach to multi-disciplinary, parametric, conceptual design
|dc.contributor.author||Angster, Scott Reed||en_US|
The use of computers in the area of design and manufacturing is commonplace in industry. Many companies are turning to custom designed in-house software to surpass the competition. A growing number are developing knowledge-based expert systems to capture the knowledge of expertise of employees before they retire.
The use of traditional artificial intelligence languages can be cumbersome to engineers who are usually familiar with traditional languages such as FORTRAN and C. The use of expert systems shells can often hinder the customization of an expert system due to limitations of the shell. An alternate approach to these methods is the use of an object-oriented framework that facilitates the creation of customized expert systems. This framework, called the Expert Consultation Environment, alleviates the programming problems of expert system development and allows the engineer to concentrate on knowledge acquisition.
This thesis describes the design of the rule classes needed by the framework. These are the base Rule class, the Equation Rule class, the Control Rule class, the Constraint Rule class and the Heuristic Rule Class. Also presented, is the development of a methodology used in creating an expert system with the framework. A prototype expert system developed using the framework for parametric, multi-disciplinary, conceptual design of aircraft is described.
|dc.subject||Expert systems (Computer science)||en_US|
|dc.title||An object-oriented, knowledge-based, non-procedural approach to multi-disciplinary, parametric, conceptual design||en_US|
|dc.description.degree||Master of Science||en_US|
|thesis.degree.name||Master of Science||en_US|
|thesis.degree.grantor||Virginia Polytechnic Institute and State University||en_US|
|dc.contributor.committeemember||Mason, William H.||en_US|
Files in this item
This item appears in the following Collection(s)
Masters Theses