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

被引：0
|
作者：
Vergez L. [1 ]
Polette A. [1 ]
Pernot J.-P. [1 ]
机构：
[1] Arts et Métiers Institute of Technology, LISPEN, HESAM Université, Aix-en-Provence
来源：
Computer-Aided Design and Applications | 2022年 / 19卷 / 04期
关键词：
CAD assembly; CAD models generation; Linkage graph; Shape synthesis;
D O I：
10.14733/cadaps.2022.722-732
中图分类号：
学科分类号：
摘要：
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 between components of different assemblies while analyzing the associated graphs. Finally, it exchanges the similar components. The similarities in a family of components must be formalized by the user. Then the similar components can be replaced by the other through smart placements. This method allows to automatically generate a wide variety of new consistent assemblies sharing the same semantics, in order to create databases of CAD assemblies ready for machine learning applications. It has been validated on several cases. © 2022 CAD Solutions, LLC.
引用
收藏
页码：722 / 732
页数：10
相关论文
共 50 条
  • [1] Automatic Layout Generation with Applications in Machine Learning Engine Evaluation
    Yang, Haoyu
    Chen, Wen
    Pathak, Piyush
    Gennari, Frank
    Lai, Ya-Chieh
    Yu, Bei
    [J]. 2019 ACM/IEEE 1ST WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD), 2019,
  • [2] On Automatic Generation of Training Images for Machine Learning in Automotive Applications
    Hsieh, Tong-Yu
    Lin, Yuan-Cheng
    Shen, Hsin-Yung
    [J]. 2019 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS 2019), 2019, : 225 - 228
  • [3] AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assemblies
    Jones, Benjamin
    Hildreth, Dalton
    Chen, Duowen
    Baran, Ilya
    Kim, Vladimir G.
    Schulz, Adriana
    [J]. ACM TRANSACTIONS ON GRAPHICS, 2021, 40 (06):
  • [4] Multi-part kinematic constraint prediction for automatic generation of CAD model assemblies using graph convolutional networks
    Vergez, Lucas
    Polette, Arnaud
    Pernot, Jean-Philippe
    [J]. CAD Computer Aided Design, 2025, 178
  • [5] An Overlay for Rapid FPGA Debug of Machine Learning Applications
    Noronha, Daniel Holanda
    Zhao, Ruizhe
    Que, Zhiqiang
    Goeders, Jeffrey
    Luk, Wayne
    Wilton, Steve
    [J]. 2019 INTERNATIONAL CONFERENCE ON FIELD-PROGRAMMABLE TECHNOLOGY (ICFPT 2019), 2019, : 135 - 143
  • [6] A Graph Machine Learning approach to Automatic Dementia Detection
    Stoppa, Edoardo
    Di Donato, Guido Walter
    Poles, Isabella
    D'Arnese, Eleonora
    Parde, Natalie
    Santambrogio, Marco Domenico
    [J]. 2023 IEEE EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS, BHI, 2023,
  • [7] Automatic Generation of Visualizations for Machine Learning Pipelines
    Liu, Lei
    Chen, Wei-Peng
    Bahrami, Mehdi
    Prasad, Mukul
    [J]. PROCEEDINGS OF THE 37TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING, ASE 2022, 2022,
  • [8] Automatic Generation of Architectural Plans with Machine Learning
    Babakhani, Reza
    [J]. TECHNOLOGY-ARCHITECTURE + DESIGN, 2023, 7 (02) : 183 - 191
  • [9] Automatic Curriculum Graph Generation for Reinforcement Learning Agents
    Svetlik, Maxwell
    Leonetti, Matteo
    Sinapov, Jivko
    Shah, Rishi
    Walker, Nick
    Stone, Peter
    [J]. THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2017, : 2590 - 2596
  • [10] Automatic Generation Algorithm for Digital Sculpture Based on CAD and Machine Vision
    Luo W.
    Qian D.
    [J]. Computer-Aided Design and Applications, 2024, 21 (S15): : 69 - 84
12345