• français
    • English
    français
  • Login
Help
View Item 
  •   Home
  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)
  • View Item
  • Home
  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Automatic CAD Assemblies Generation by Linkage Graph Overlay for Machine Learning Applications

Communication avec acte
Author
VERGEZ, Lucas
ccPERNOT, Jean-Philippe
ccPOLETTE, Arnaud
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]

URI
http://hdl.handle.net/10985/23482
DOI
10.14733/cadconfp.2021.46-50
Date
2021-07-05

Abstract

Enlarging 3D model databases by shape synthesis is a large field of research. Indeed, the use of machine learning techniques requires a huge amount of labeled CAD models, and it is therefore crucial to rely on large and varied databases. Most of existing works in shape synthesis focus on everyday life objects generation [1]. However, these methods often do not work on assemblies composed of several CAD models, and it is the aim of this paper to develop a new shape synthesis method to enlarge existing CAD assembly databases. Today, there exist lots of free databases of non-labeled CAD models (e.g. GrabCAD, 3D Warehouse, Turbosquid) often available as STEP or IGES files. Unfortunately, very few of these databases are labeled. Other databases like PartNet [2] and ShapeNet [3] are currently labeled by crowdsourcing, but they do not contain complex mechanical assemblies. Current works in shape synthesis often use auto-encoders to generate new coherent CAD assemblies [4]. Moreover, there are probabilistic models to create diversity in large 3D Database [5] or to classify 3D assemblies [6]. Those techniques often use linkage graphs [7] to classify and generate new coherent assemblies. Furthermore, information within the linkage graphs differs according to the method, and those graphs are not suitable for complex CAD assemblies like hydraulic pumps. But the main issue of all methods is still that those databases have to be labelled. The method explained in this paper consists in creating new labeled CAD assemblies from existing ones by linkage graph overlay. Here, the STEP file format has been adopted in order to be the most reproductible and to be adaptable. The linkage graphs are automatically created thanks to the identification of the linkages between the components. Indeed, linkages are not included in the STEP files and they need to be computed. Theses linkage graphs are then analyzed and components with similar linkages are detected. Finally, once the similarities detected, the corresponding components can be exchanged to created new assemblies for which the labels can be directly inherited from the source assemblies. The contribution is threefold: (i) a method to create linkage graphs from existing non-labelled CAD assemblies; (ii) a method to recognize basic components using linkage graphs; (iii) a smart overlay method to replace some components while keeping the coherence between all the components of the assembly. The algorithm has been implemented in Python on FreeCAD and it has been tested on several test cases. Figure 1 shows the overview of the method, from the graph synthesis to the components overlay, finishing with the replacement of the components. The results are presented and discussed, and a conclusion ends this extended abstract while discussing the next steps.

Files in this item

Name:
LISPEN_CAD_2021_PERNOT.pdf
Size:
750.9Kb
Format:
PDF
View/Open

Collections

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

Related items

Showing items related by title, author, creator and subject.

  • Automatic CAD Assemblies Generation by Linkage Graph Overlay for Machine Learning Applications 
    Article dans une revue avec comité de lecture
    VERGEZ, Lucas; ccPERNOT, Jean-Philippe; ccPOLETTE, Arnaud (CAD Solutions, LLC, 2021-11-29)
    This paper introduces an approach to synthetize new CAD assemblies from existing STEP files. The algorithm first generates linkage graph by detecting linkage between components. Then it detects linkages similarities ...
  • Multi-part kinematic constraint prediction for automatic generation of CAD model assemblies using graph convolutional networks 
    Article dans une revue avec comité de lecture
    VERGEZ, Lucas; ccPOLETTE, Arnaud; ccPERNOT, Jean-Philippe (Elsevier BV, 2025-01)
    This paper presents a machine learning-based approach to predict kinematic constraints between CAD models that have potentially never been assembled together before. During the learning phase, the algorithm is trained ...
  • Interface-Based Search and Automatic Reassembly of CAD Models for Database Expansion and Model Reuse 
    Article dans une revue avec comité de lecture
    VERGEZ, Lucas; ccPOLETTE, Arnaud; ccPERNOT, Jean-Philippe (Elsevier BV, 2024-02)
    This paper introduces a new framework for reassembling CAD models of mechanical parts. The generated CAD assemblies are well-constrained, with collision-free parts, and they are ready-to-use for downstream applications. ...
  • Survey on the View Planning Problem for Reverse Engineering and Automated Control Applications 
    Article dans une revue avec comité de lecture
    PEUZIN-JUBERT, Manon; NOZAIS, Dominique; MARI, Jean-Luc; ccPERNOT, Jean-Philippe; ccPOLETTE, Arnaud (Elsevier BV, 2021-12)
    At present, optical sensors are being widely used to realize high quality control or reverse engineering of products, systems, buildings, environments or human bodies. Although the intrinsic characteristics of such ...
  • A Data Structure for Developing Data-Driven Digital Twins 
    Ouvrage scientifique
    ORUKELE, Oghenemarho; ccPOLETTE, Arnaud; GONZALEZ LORENZO, Aldo; MARI, Jean-Luc; ccPERNOT, Jean-Philippe (Springer Nature Switzerland, 2024-06)
    Digital twins have the potential to revolutionize the way we design, build and maintain complex systems. They are high-fidelity representations of physical assets in the digital space and thus allow advanced simulations ...

Browse

All SAMCommunities & CollectionsAuthorsIssue DateCenter / InstitutionThis CollectionAuthorsIssue DateCenter / Institution

Newsletter

Latest newsletterPrevious newsletters

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales