A quantitative analysis of parametric CAD model complexity and its relationship to perceived modeling complexity
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Título
A quantitative analysis of parametric CAD model complexity and its relationship to perceived modeling complexityFecha de publicación
2023Editor
ElsevierCita bibliográfica
CONTERO, Manuel, et al. A quantitative analysis of parametric CAD model complexity and its relationship to perceived modeling complexity. Advanced Engineering Informatics, 2023, vol. 56, p. 101970.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.sciencedirect.com/science/article/pii/S1474034623000988Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Digital product data quality and reusability has been proven a critical aspect of the Model-Based Enterprise to
enable the efficient design and redesign of products. The extent to which a history-based parametric CAD ... [+]
Digital product data quality and reusability has been proven a critical aspect of the Model-Based Enterprise to
enable the efficient design and redesign of products. The extent to which a history-based parametric CAD model
can be edited or reused depends on the geometric complexity of the part and the procedure employed to build it.
As a prerequisite for defining metrics that can quantify the quality of the modeling process, it is necessary to have
CAD datasets that are sorted and ranked according to the complexity of the modeling process. In this paper, we
examine the concept of perceived CAD modeling complexity, defined as the degree to which a parametric CAD
model is perceived as difficult to create, use, and/or modify by expert CAD designers. We present a novel method
to integrate pair-wise comparisons of CAD modeling complexity made by experts into a single metric that can be
used as ground truth. Next, we discuss a comprehensive study of quantitative metrics which are derived primarily from the geometric characteristics of the models and the graph structure that represents the parent/child
relationships between features. Our results show that the perceived CAD modeling complexity metric derived
from experts’ assessment correlates particularly strongly with graph-based metrics. The Spearman coefficients
for five of these metrics suggest that they can be effectively used to study the parameters that influence the
reusability of models and as a basis to implement effective personalized learning strategies in online CAD
training scenarios. [-]
Publicado en
Advanced Engineering Informatics, 2023, vol. 56, p. 101970.Entidad financiadora
Universitat Politècnica de València | Ministerio de Universidades
Código del proyecto o subvención
PAID-11-21 | PRX21-00387
Derechos de acceso
info:eu-repo/semantics/openAccess
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Excepto si se señala otra cosa, la licencia del ítem se describe como: 1474-0346/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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