A small feature suppression/unsuppression system for preparing B-rep models for analysis

  • Authors:
  • K. Y. Lee;C. G. Armstrong;M. A. Price;J. H. Lamont

  • Affiliations:
  • Queen's University, Belfast;Queen's University, Belfast;Queen's University, Belfast;TranscenData Europe Ltd., Cambridge

  • Venue:
  • Proceedings of the 2005 ACM symposium on Solid and physical modeling
  • Year:
  • 2005

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Abstract

CAD technology plays an ever more central role in today's multidisciplinary simulation environments. While this has enabled highly complex and detailed models to be used earlier in the design process it has brought with it difficulties for simulation specialists. Most notably CAD models now contain many details which are irrelevant to simulation disciplines. CAD systems have feature trees which record feature creation but unfortunately this does not capture which features are relevant to which analysis discipline. Many features of little significance to an analysis only emerge during the construction of the model. The ability to selectively suppress and reinstate features while maintaining an audit trail of changes is required to facilitate the control of the idealisation process. Features suppressed for one analysis can be retrieved for use in another.This work uses combinatorial topology concepts to outline the necessary conditions so that CAD model simplification operations can be designed as continuous transformations. Irrelevant features can then be suppressed and subsequently reinstated, within defined limitations, independently from the order in which they were suppressed. The implementation of these concepts provides analysts with a mechanism for generating analysis models with different levels of detail, without having to repeat the simplification process from the original CAD geometry. Most importantly, the information recorded during the suppress operations forms an essential audit trail of the idealisation process and can be presented in a feature-tree like structure allowing analysts to review their modelling decisions retrospectively. The approach also facilitates the generation of local, detailed models encapsulating a feature of interest. The proposed system follows a Find and Fix paradigm; where different algorithms for feature finding and fixing can be utilised in a common cellular modelling framework.