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Geometric Over-Constraints Detection: A Survey

Article dans une revue avec comité de lecture
Auteur
HU, Hao
ZHANG, Chao
HUANG, Yanjia
ZHAO, Qian
YEUNG, Sunny
ccPERNOT, Jean-Philippe
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
ccKLEINER, Mathias

URI
http://hdl.handle.net/10985/22856
DOI
10.1007/s11831-020-09509-y
Date
2021-05-11
Journal
Archives of Computational Methods in Engineering

Résumé

Currently, geometric over-constraints detection is of major interest in several diferent felds. In terms of product development process (PDP), many approaches exist to compare and detect geometric over-constraints, to decompose geometric systems, to solve geometric constraints systems. However, most approaches do not take into account the key characteristics of a geometric system, such as types of geometries, diferent levels at which a system can be decomposed e.g numerical or structural. For these reasons, geometric over-constraints detection still faces challenges to fully satisfy real needs of engineers. The aim of this paper is to review the state-of-the-art of works involving with geometric over-constraints detection and to identify pos sible research directions. Firstly, the paper highlights the user requirements for over-constraints detection when modeling geometric constraints systems in PDP and proposes a set of criteria to analyze the available methods classifed into four categories: level of detecting over-constraints, system decomposition, system modeling and results generation. Secondly, it introduces and analyzes the available methods by grouping them based on the introduced criteria. Finally, it discusses pos sible directions and future challenges.

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LISPEN_ACME_2020_PERNOT.pdf
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  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)

Documents liés

Visualiser des documents liés par titre, auteur, créateur et sujet.

  • On the detection of over-constrained subparts of configurations when deforming free-form curves 
    Communication avec acte
    HU, Hao; ccPERNOT, Jean-Philippe; ccKLEINER, Mathias (2016)
    Today, designers use CAD modelers to define and modify NURBS surfaces involved in the design of complex shapes like car bodies or turbine blades. The generated shapes often result from the use of variational modeling ...
  • Over-constraints detection and resolution in geometric equation systems 
    Article dans une revue avec comité de lecture
    HU, Hao; ccPERNOT, Jean-Philippe; ccKLEINER, Mathias (Elsevier, 2017)
    This paper proposes an original decision-support approach to address over-constrained geometric configurations in Computer-Aided Design. It focuses particularly on the detection and resolution of redundant and conflicting ...
  • eCAD-Net: Editable Parametric CAD Models Reconstruction from Dumb B-Rep Models Using Deep Neural Networks 
    Article dans une revue avec comité de lecture
    ZHANG, Chao; PINQUIE, Romain; CARASI, Gregorio; DE CHARNACE, Henri; ccPERNOT, Jean-Philippe (Elsevier BV, 2025-01)
    This paper introduces a novel framework capable of reconstructing editable parametric CAD models from dumb B-Rep models. First, each B-Rep model is represented with a network-friendly formalism based on UVgraph, which ...
  • Automatic 3D CAD models reconstruction from 2D orthographic drawings 
    Article dans une revue avec comité de lecture
    ZHANG, Chao; ccPOLETTE, Arnaud; CARASI, Gregorio; DE CHARNACE, Henri; ccPERNOT, Jean-Philippe (2023)
    This paper introduces a two-stage approach that automatically generates 3D CAD models from 2D orthographic drawings. First, a pattern-matching algorithm is proposed to reconstruct a network of 3D edges by matching 2D ...
  • Geometric Over-Constraints Detection: A Survey 
    Article dans une revue avec comité de lecture
    HUANG, Yanjia; ZHAO, Qian; YEUNG, Sunny (Springer Science and Business Media LLC, 2021-05-11)
    Currently, geometric over-constraints detection is of major interest in several different fields. In terms of product development process (PDP), many approaches exist to compare and detect geometric over-constraints, to ...

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