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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Main Author: | |
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Format: | Thesis |
Language: | English |
Published: |
2008
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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. |
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