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    Discovery of Mental Metadata Used for Analogy Formation in Function-Based Design

    Source: Journal of Mechanical Design:;2016:;volume( 138 ):;issue: 010::page 101110
    Author:
    Arlitt, Ryan M.
    ,
    Nix, Anthony A.
    ,
    Sen, Chiradeep
    ,
    Stone, Robert B.
    DOI: 10.1115/1.4034090
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Applying previous solutions to solve new problems is a core aspect of design, and designers routinely use informal analogies to solve a wide variety of design problems. However, when the goal is to consider a large quantity and variety of creative solutions, relying on informal analogy recall may limit the analogy and solution breadth. This paper reports on work to identify the analogy connections that designers make during concept generation such that computational support can be employed to intentionally retrieve analogical solutions from existing systems. A study of the types of similarity that are commonly used to draw design analogies, and whether some types of similarity are used more frequently in compound analogy versus single analogy, was designed and implemented. The experiment consists of a design task and a follow up interview. Ten mechanical engineering graduate students specializing in design participated. Eight different types of analogical similarity are observed, and each type is equally likely to be used to form either single or compound analogies. Notably, the flow behavior was a commonly observed type of abstract similarity that helped designers notice connections across domains, suggesting the value of capturing and retrieving (computationally) flow behavior abstractions for the purpose of relating systems analogically.
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      Discovery of Mental Metadata Used for Analogy Formation in Function-Based Design

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4234866
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    contributor authorArlitt, Ryan M.
    contributor authorNix, Anthony A.
    contributor authorSen, Chiradeep
    contributor authorStone, Robert B.
    date accessioned2017-11-25T07:17:57Z
    date available2017-11-25T07:17:57Z
    date copyright2016/08/30
    date issued2016
    identifier issn1050-0472
    identifier othermd_138_10_101110.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4234866
    description abstractApplying previous solutions to solve new problems is a core aspect of design, and designers routinely use informal analogies to solve a wide variety of design problems. However, when the goal is to consider a large quantity and variety of creative solutions, relying on informal analogy recall may limit the analogy and solution breadth. This paper reports on work to identify the analogy connections that designers make during concept generation such that computational support can be employed to intentionally retrieve analogical solutions from existing systems. A study of the types of similarity that are commonly used to draw design analogies, and whether some types of similarity are used more frequently in compound analogy versus single analogy, was designed and implemented. The experiment consists of a design task and a follow up interview. Ten mechanical engineering graduate students specializing in design participated. Eight different types of analogical similarity are observed, and each type is equally likely to be used to form either single or compound analogies. Notably, the flow behavior was a commonly observed type of abstract similarity that helped designers notice connections across domains, suggesting the value of capturing and retrieving (computationally) flow behavior abstractions for the purpose of relating systems analogically.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleDiscovery of Mental Metadata Used for Analogy Formation in Function-Based Design
    typeJournal Paper
    journal volume138
    journal issue10
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4034090
    journal fristpage101110
    journal lastpage101110-11
    treeJournal of Mechanical Design:;2016:;volume( 138 ):;issue: 010
    contenttypeFulltext
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