Dimension and tolerance modeling and transformations in feature based design and manufacturing

Jami J. Shah, Yong Yan, Bing Chun Zhang

Research output: Contribution to journalArticle

60 Citations (Scopus)

Abstract

This paper describes the development and application of a geometric dimension and tolerance (GD&T) model for use in both design and process planning. The model meets criteria for computability and compatibility with the Y14.5M standard. The model is first used to capture the designers GD&T scheme on a feature based design model, validate its completeness, and then transfer the GD&T to machining features extracted automatically by feature recognition. The model is based on relative degrees of freedom of geometric entities: feature axes, edges, faces and features-of-size. Dimension graphs are created based on the degrees of freedom for each control direction. Datum reference frames and standard tolerance classes are incorporated into the graph. The model allows dimension specification, dimension scheme modification, and dimension scheme validation. A methodology to automatically determine the GD&T of machining volumes obtained by feature recognition is also described.

Original languageEnglish (US)
Pages (from-to)475-488
Number of pages14
JournalJournal of Intelligent Manufacturing
Volume9
Issue number5
StatePublished - 1998

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Machining
Process planning
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Keywords

  • Computer-aided process planning (CAPP)
  • Feature mapping
  • Feature modeling
  • Geometric tolerancing
  • Solid modeling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Industrial and Manufacturing Engineering

Cite this

Dimension and tolerance modeling and transformations in feature based design and manufacturing. / Shah, Jami J.; Yan, Yong; Zhang, Bing Chun.

In: Journal of Intelligent Manufacturing, Vol. 9, No. 5, 1998, p. 475-488.

Research output: Contribution to journalArticle

Shah, Jami J. ; Yan, Yong ; Zhang, Bing Chun. / Dimension and tolerance modeling and transformations in feature based design and manufacturing. In: Journal of Intelligent Manufacturing. 1998 ; Vol. 9, No. 5. pp. 475-488.
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