Assembly simulation of multi-branch cables

被引:18
|
作者
Lv, Naijing [1 ]
Liu, Jianhua [1 ]
Ding, Xiaoyu [1 ]
Lin, Haili [1 ]
机构
[1] Beijing Inst Technol, Sch Mech Engn, 5 South Zhongguancun St, Beijing 100081, Peoples R China
基金
中国国家自然科学基金;
关键词
Assembly simulation; Multi-branch cable; Physically based modeling; Cosserat theory; Elastic rod model; INTERACTIVE SIMULATION; DYNAMICS; PRODUCT; REALITY; DESIGN; PARTS;
D O I
10.1016/j.jmsy.2017.09.007
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Cable assembly simulation is a key issue in the computer-aided design (CAD) of products with complex electrical components. In this study, an assembly simulation method is developed to simulate the assembly process of multi-branch cables. First, based on the Cosserat theory of elastic rods, a novel scheme is introduced to model the joints of multi-branch cables. The potential energy of joints is calculated by taking the topology and anatomical features into consideration. Various physical properties are considered. Various constraints, including connectors, collars, and handles are analyzed, based on which the initial conditions of assembly simulation are determined. The configuration of the cable is then calculated by minimizing its potential energy. To increase computational efficiency, GPU acceleration is introduced, which makes the simulation run at interactive rates even for a cable with resolution up to 1000 discrete points. Finally, the proposed algorithm is integrated into the commercial assembly simulation system, DELMIA. Several simulations were performed with our system. It was demonstrated that the proposed method is able to handle cables with complex topologies. In addition, the proposed method is about four times as efficient as a previous method, and it is able to generate realistic configurations of multi-branch cables at interactive rates. Thus, the proposed method is helpful in the assembly process planning of cables. (c) 2017 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:201 / 211
页数:11
相关论文
共 50 条
  • [41] Multi-Branch U-Net for Interactive Segmentation
    Li, Zhicheng
    Wang, Tao
    Mei, Chun
    Pei, Zhenyu
    IEEE SIGNAL PROCESSING LETTERS, 2024, 31 : 974 - 978
  • [42] A 400 GHz Broadband Multi-Branch Waveguide Coupler
    Niu, Zhongqian
    Zhang, Bo
    Zhou, Zhen
    A, Lixin
    Wang, Yiwei
    Chen, Xingyu
    He, Yizheng
    Hu, Yi
    Chen, Xiaoming
    Zhang, Jicong
    2019 12TH UK-EUROPE-CHINA WORKSHOP ON MILLIMETER WAVES AND TERAHERTZ TECHNOLOGIES (UCMMT), 2019,
  • [43] MBFFNet: Multi-Branch Feature Fusion Network for Colonoscopy
    Su, Houcheng
    Lin, Bin
    Huang, Xiaoshuang
    Li, Jiao
    Jiang, Kailin
    Duan, Xuliang
    FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2021, 9
  • [44] Performance Evaluation of a Multi-Branch Tree Algorithm in RFID
    Cui, Yinghua
    Zhao, Yuping
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2010, 58 (05) : 1356 - 1364
  • [45] MBFFNet: Multi-Branch Feature Fusion Network for Colonoscopy
    Su, Houcheng
    Lin, Bin
    Huang, Xiaoshuang
    Li, Jiao
    Jiang, Kailin
    Duan, Xuliang
    Frontiers in Bioengineering and Biotechnology, 2021, 9
  • [46] Multi-branch fusion network for hyperspectral image classification
    Gao, Hongmin
    Yang, Yao
    Lei, Sheng
    Li, Chenming
    Zhou, Hui
    Qu, Xiaoyu
    KNOWLEDGE-BASED SYSTEMS, 2019, 167 : 11 - 25
  • [47] Multi-Branch Network for Few-shot Learning
    Ren, Kai
    Guo, Zijie
    Zhang, Zhimin
    Zhu, Rui
    Li, Xiaoxu
    PROCEEDINGS OF 2022 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC), 2022, : 520 - 525
  • [48] Study of multi-branch structure of Universal Learning Networks
    Mabu, Shingo
    Shimada, Kaoru
    Hirasawa, Kotaro
    Hu, Jinglu
    APPLIED SOFT COMPUTING, 2009, 9 (01) : 393 - 403
  • [49] COMPOUND MULTI-BRANCH FEATURE FUSION FOR IMAGE DERAINDROP
    Fan, Chi-Mao
    Liu, Tsung-Jung
    Liu, Kuan-Hsien
    2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, 2023, : 3399 - 3403
  • [50] Multi-branch Bounding Box Regression for Object Detection
    Yuan, Hui-Shen
    Chen, Si-Bao
    Luo, Bin
    Huang, Hao
    Li, Qiang
    COGNITIVE COMPUTATION, 2023, 15 (04) : 1300 - 1307