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Persistent naming through persistent entities

Authors: Rafael Bidarra; Willem F. Bronsvoort;

Persistent naming through persistent entities

Abstract

Current parametric modeling systems suffer from the persistent naming problem, which is responsible for the unpredictable, sometimes stunning, behavior of such systems when re-evaluating a model, even after simple editing operations. This paper claims that the problem is an inherent difficulty of history-based parametric modeling, and that it is of little use to insist on developing more and more persistent naming schemes which end up solving only a fraction of the problem. Instead, it is argued that the rationale behind such schemes should itself be revised. Alternative approaches to define a parametric model based on persistent parametric entities can, in fact, eliminate the use of references to non-persistent geometric model entities, which is the cause of the problem. One such approach is described here, which is able to take full advantage of parametric solid modeling. It provides persistent entities in the parametric definition domain, which can be safely and consistently referred to. A number of examples illustrate how user specification of modeling operations can be performed through the interaction with a declarative feature model.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
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!
4
Average
Top 10%
Average
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