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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao The Visual Computerarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
The Visual Computer
Article . 1987 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
DBLP
Article . 1987
Data sources: DBLP
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Tolerances in computer-aided geometric design

Authors: Joshua U. Turner; Michael J. Wozny;

Tolerances in computer-aided geometric design

Abstract

In the design of discrete part shapes, the specification of tolerance constraints can have major consequences for product quality and cost. Traditional methods for tolerance analysis and synthesis are time-consuming and have limited applicability. The thesis of this work is that geometric design systems based on solid modeling technology can be used to automate the solution of these problems. First, a mathematical theory of tolerances is developed. It is shown that a tolerance specification may be expressed as an "in-tolerance" region of a normed vector space over the reals. A representative selection of both dimensional (plus-minus) and geometric tolerance types is investigated. Using this theoretical framework, methods are developed for the solution of tolerance analysis and synthesis problems. Three methods for tolerance analysis are presented: a linear programming method, a Monte Carlo method, and a least squares fitting method. These methods differ as to linearity assumptions and computational costs. The linear programming method supports a worst-case solution basis. The other two methods also support the solution of tolerance analysis problems on a statistical basis. Next, models are developed for worst-case and statistical tolerance synthesis. It is shown that at least in some cases convex programming methods may be applicable. For both the analysis and the synthesis methods, all necessary geometric relationships are automatically derived from the geometric model. To provide a computational framework, a general strategy for constructive variational geometry is developed. This includes a general scheme for the relative positioning of parts and part features. Three relative positioning operators are described. Methods are given for the modeling of size, orientation, and position variations. Feasibility is demonstrated using an experimental geometric modeling system named GEOTOL. The linear programming and Monte Carlo methods are used in solving tolerance analysis problems for several simple assemblies, as well as for a larger bus bar assembly drawn from an actual product. It is shown that three-dimensional tolerancing problems can be solved by these methods.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
53
Top 10%
Top 1%
Average
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