Intelligent DMU Creation: Toleranced Part Modelling to Enhance the Digital Environment

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    TThis paper describes a method for the consideration of tolerance stack ups in the computer-aided design (CAD) environment as design concepts are developed into digital mock ups (DMUs). The method functionality includes the capability to create maximum (MMC) and least material condition (LMC) versions of the nominally sized components, allowing the three sets of entities to co-exist while respecting the positional constraints of the nominal master model. As the user switches between MMC and LMC combinations across a number of components, the overall dimensions of the assembly within the DMU change accordingly. The assembly constraints are regenerated through an equivalencing method based on surface properties, to respect the assembly intention. The new DMU, therefore, is an improved reflection of ‘as manufactured’ part forms making assembly analysis and the allocation of tolerances more accurate at the conceptual design stage, a novel function not currently available in commercial CAD software.

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    • Intelligent DMU creation: Toleranced part modelling to enhance the digital environment

      Rights statement: © 2017 The Authors. This is an open access article published under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.

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    DOI

    Original languageEnglish
    Number of pages6
    Pages (from-to)92-97
    JournalProcedia CIRP
    Journal publication date2017
    Volume60
    Early online date09 May 2017
    DOIs
    Publication statusPublished - 2017
    Event27th CIRP Design Conference - Vincent Building, Cranfield University, Cranfield, United Kingdom
    Duration: 10 May 201712 May 2017
    https://www.cranfield.ac.uk/events/events-2017/cirp-2017

      Research areas

    • Design for manufacture; Design for assembly; Tolerance design

    ID: 129788252