Strategies for Using Feature Information in Model Simplification

نویسندگان

  • Yogesh H Kulkarni
  • Anil Sahasrabudhe
  • Mukund Kale
چکیده

At an early stage of design, models created by Computer Aided Design (CAD) applications are not sent as-is for analysis in Computer Aided Engineering (CAE) applications, especially the non-trivial models. Analysis engineers use their domain expertise to simplify these models so that analysis gets performed with lesser resources without compromising much on accuracy of the results. Such Model Simplification involves suppressing small-irrelevant features, dimension reduction, use of symmetry-patterns etc. This process is still widely a manual process and has lot of potential for automation. Most of the current automation approaches work on the final CAD geometry. Only a few attempts seem to have been made which use feature information effectively. CAD Feature information, which used to be hidden due to its proprietary nature, has started becoming available in the contemporary CAD applications. Features carry higher level information, such as Designer’s intent, than what’s available in the geometry-topology (Brep Boundary representation). One of the major tasks in Model Simplification involves suppressing Holes, Fillets, and Chamfers under certain parameters. Model simplification becomes far more straightforward using feature information as features are readily available to be suppressed; which is not possible with the plain Solid Model. In Model Simplification one can leverage use of features such as Pattern, Mirror to detect symmetry and just keep the master portion for the analysis. In Dimension Reduction aspect of Model Simplification models which are inherently of slender or thin-wall in nature are idealized to Curves or Midsurfaces to be used for Beam or Shell elements respectively. This paper surveys various attempts of Model Simplifications using feature information. It lists issues faced and explores possibility of addressing some of them by a new-proposed technique. In this, high level algorithms for de-featuring and Midsurface generation are elaborated. Midsurface is built on per-feature basis and is connected looking at the feature interactions. The process is elaborated with a step-by-step example.

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تاریخ انتشار 2013