'Creation of parametric models of design drawings based on their adaptive backbone network' Software systems and computational methods nbpublish.com
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Lyachek Yu.T., Cheranova S.I., Abdulkader B.A. Creation of parametric models of design drawings based on their adaptive backbone network

Published in journal "Software systems and computational methods", 2013-2 in rubric "Computer graphics, image processing and pattern recognition", pages 181-191.

Resume: The author focuses on the special aspects and algorithms of automatic generation of a parametric model of a non-parametric description of the drawing based on the adaptive core network of the drawing that changes depending on the size values used. Adaptive network serves as an effective universal parametric model of the drawing as it applies to the entire drawing and therefore to the all important points of the basic graphic primitives, that defines the shape of the item shown in the drawing. At the same time all elements of the drawing specification (various technological keys) are indirectly related to that network. This allows to automatically build almost complete design drawings of modified items. Building of the adaptive core network depends only on the basic graphical elements, view and sizes used. It is not depended on the order and way of formation of graphic primitives (the algorithm of constricting the item), and time and means of dimensional notating of the drawing. That makes it possible to use such network to automatically generate the parametric description of almost any design drawing previously build in the non-parametric systems. It requires only clear dimensional notation for the shape of the item, i.e. the original drawing must meet the requirements of Unified system for design documentation allowing to produce the item. The software systems based on the adaptive network may find wide use in the development departments of engineering design offices and businesses.

Keywords: Software, parameterization, adaptation, basic, network, drawing, type, model, algorithm, relation.

DOI: 10.7256/2305-6061.2013.2.7470

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