Using a Design Repository to Drive Concept Generation
This paper describes how a design repository can be used as a concept generation tool by drawing upon archived function-based design knowledge. Modern design methodologies include several types of activities to formally generate design concepts. Typical concept generation methods range from open-end...
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Veröffentlicht in: | Journal of computing and information science in engineering 2008-03, Vol.8 (1), p.014502 (8)-014502 (8) |
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creator | Bohm, Matt R Vucovich, Jayson P Stone, Robert B |
description | This paper describes how a design repository can be used as a concept generation tool by drawing upon archived function-based design knowledge. Modern design methodologies include several types of activities to formally generate design concepts. Typical concept generation methods range from open-ended creative brainstorming activities to quantitative function-component analysis. A combination of two such methods—the chi-matrix and morphological matrix techniques—is the basis for this work. Building on existing functionality of the design repository, desired product functions can be specified in a search of stored design knowledge, returning a morphological matrix of artifacts solving the specified functions. Such a search is termed a morphological search. The repository morphological search feature is evaluated against concepts generated in a previous original design project. Results of the morphological search return are then compared to ten of the original concept variants generated during the design project. This comparison shows that 89% of the specified subfunctions return results and that, on average, 77% of the components used in the hand-generated concepts can be derived by using the morphological search feature. |
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Modern design methodologies include several types of activities to formally generate design concepts. Typical concept generation methods range from open-ended creative brainstorming activities to quantitative function-component analysis. A combination of two such methods—the chi-matrix and morphological matrix techniques—is the basis for this work. Building on existing functionality of the design repository, desired product functions can be specified in a search of stored design knowledge, returning a morphological matrix of artifacts solving the specified functions. Such a search is termed a morphological search. The repository morphological search feature is evaluated against concepts generated in a previous original design project. Results of the morphological search return are then compared to ten of the original concept variants generated during the design project. 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Results of the morphological search return are then compared to ten of the original concept variants generated during the design project. 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Comput. Inf. Sci. Eng</stitle><date>2008-03-01</date><risdate>2008</risdate><volume>8</volume><issue>1</issue><spage>014502 (8)</spage><epage>014502 (8)</epage><pages>014502 (8)-014502 (8)</pages><issn>1530-9827</issn><eissn>1944-7078</eissn><abstract>This paper describes how a design repository can be used as a concept generation tool by drawing upon archived function-based design knowledge. Modern design methodologies include several types of activities to formally generate design concepts. Typical concept generation methods range from open-ended creative brainstorming activities to quantitative function-component analysis. A combination of two such methods—the chi-matrix and morphological matrix techniques—is the basis for this work. Building on existing functionality of the design repository, desired product functions can be specified in a search of stored design knowledge, returning a morphological matrix of artifacts solving the specified functions. Such a search is termed a morphological search. The repository morphological search feature is evaluated against concepts generated in a previous original design project. Results of the morphological search return are then compared to ten of the original concept variants generated during the design project. This comparison shows that 89% of the specified subfunctions return results and that, on average, 77% of the components used in the hand-generated concepts can be derived by using the morphological search feature.</abstract><pub>ASME</pub><doi>10.1115/1.2830844</doi></addata></record> |
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title | Using a Design Repository to Drive Concept Generation |
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