A fuzzy approach to support DFA evaluation of design concepts

Design evaluation form one of the more important aspects in determining whether it has met the initial requirements. Post design evaluations however are less advantageous than those made in the earlier stage of design, since it provides for ample opportunity to make less costly changes to the des...

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Bibliographic Details
Main Author: Omar, Badrul
Format: Thesis
Language:English
Published: 2008
Subjects:
Online Access:http://eprints.uthm.edu.my/7191/1/24p%20BADRUL%20OMAR.pdf
http://eprints.uthm.edu.my/7191/
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Summary:Design evaluation form one of the more important aspects in determining whether it has met the initial requirements. Post design evaluations however are less advantageous than those made in the earlier stage of design, since it provides for ample opportunity to make less costly changes to the design. During conceptual design stage, the knowledge and information about the design is often vague and incomplete and this makes evaluation even more difficult. At present there are not enough tools to support the designer to make evaluations on design concepts. This thesis presents an approach which will support designer doing evaluation on design concepts by incorporating DFA criteria into the evaluating tool. The criteria most useful at that stage would be the part count reduction analysis. The handling of the information and knowledge at this conceptual stage will be handled by a fuzzy logic expert system. A demonstration on the usefulness of fuzzy logic together with the part count analysis was done on two case studies. The first use the approach to demonstrate the way it can support the designers at the concepts selection stage and the second examines the redesign of an existing product. The result of the case studies shows that it is possible to integrate the use of fuzzy logic with DFA in providing support to the designer in doing design concepts evaluation. This approach also highlights the ability of fuzzy logic in representing information and knowledge at this conceptual stage in the form of fuzzy sets.