A quantitative analysis of parametric CAD model complexity and its relationship to perceived modeling complexity
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comunitat-uji-handle2:10234/7035
comunitat-uji-handle3:10234/8617
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Title
A quantitative analysis of parametric CAD model complexity and its relationship to perceived modeling complexityDate
2023Publisher
ElsevierBibliographic citation
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.Type
info:eu-repo/semantics/articlePublisher version
https://www.sciencedirect.com/science/article/pii/S1474034623000988Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
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. [-]
Is part of
Advanced Engineering Informatics, 2023, vol. 56, p. 101970.Funder Name
Universitat Politècnica de València | Ministerio de Universidades
Project code
PAID-11-21 | PRX21-00387
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info:eu-repo/semantics/openAccess
This item appears in the folowing collection(s)
- EMC_Articles [825]
Except where otherwise noted, this item's license is described as 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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