Relaxed parametric design with probabilistic constraints

Yaacov Hel-Or*, Ari Rappoport, Michael Werman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


Parametric design is an important modeling paradigm in computer aided design. Relationships (constraints) between the degrees of freedom (DOFs) of the model, instead of the DOFs themselves, are specified, resulting in efficient design modifications and variations. Current parametric modelers require an exact specification of all the constraints involved, which causes overwork on the part of the designer during design iterations. We describe the relaxed parametric design modeling paradigm, in which decisions which needlessly limit the freedom of design in later stages are avoided. The designer uses soft constraints and specifies the exactness by which they are to be met. As a specific scheme for implementing relaxed parametric design, we present probabilistic constraints, where a parametric model is viewed as a stochastic process. Softness of a constraint is represented as the covariance of a suitably distributed random variable. We describe a novel method for expressing the DOFs and the model as a system of probabilistic equations, which is then solved using the Kalman filter, a powerful estimation tool for stochastic systems. An a priori covariance matrix associated with a DOF can be used as a guideline to the solver to select a particular solution among multiple solutions.

Original languageAmerican English
Title of host publicationProc 2 Symp Solid Model Appl
PublisherPubl by ACM
Number of pages10
ISBN (Print)0897915844, 9780897915847
StatePublished - 1993
EventProceedings of the 2nd Symposium on Solid Modeling and Applications - Montreal, Que, Can
Duration: 19 May 199321 May 1993

Publication series

NameProc 2 Symp Solid Model Appl


ConferenceProceedings of the 2nd Symposium on Solid Modeling and Applications
CityMontreal, Que, Can


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