Choosing Virtual Assembly Basic Component Based on the K-Means Clustering Algorithm and the Digraph Analysis

被引:0
|
作者
Mao, Zhao Yong [1 ]
Fan, Yu [1 ]
Wang, Xi [2 ]
机构
[1] Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Peoples R China
[2] CAAC, Northwest Reg Air Traff Management Bur, Xian 710072, Peoples R China
来源
MANUFACTURING, DESIGN SCIENCE AND INFORMATION ENGINEERING, VOLS I AND II | 2015年
关键词
Virtual Reality Technology; Digraph; K-Means Clustering Algorithm; Virtual Maintenance; GENETIC ALGORITHM;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Virtual assembly process planning (VAPP) is one of the most important technologies of virtual assembly, and seeking the basic component is the most basic step of virtual assembly process planning, using this kind of technology can not only reduce the cost of traditional assembly training, but also achieve the high efficient maintenance purpose. In order to realize the automatic VAPP, this paper proposes a method that based on a clustering algorithm and a digraph analysis method. Here, this paper introduce the K-Means clustering algorithm into three dimensional space objects clustering calculation, and K-Means is usually used in two dimensional space. Then, the digraph analysis method (DAM) is been used to judge which one will be the most reasonable choice for the basic component among all the choices that have been calculated by the K-Means algorithm. Finally we reuse the K-Means algorithm and the digraph analysis method for several times to find the basic components for each assembly step. Thus the virtual assembly process sequence can be extracted automatically.
引用
收藏
页码:343 / 350
页数:8
相关论文
共 50 条
  • [41] Clustering stability-based Evolutionary K-Means
    He, Zhenfeng
    Yu, Chunyan
    SOFT COMPUTING, 2019, 23 (01) : 305 - 321
  • [42] Clustering stability-based Evolutionary K-Means
    Zhenfeng He
    Chunyan Yu
    Soft Computing, 2019, 23 : 305 - 321
  • [43] Content-based image retrieval using PSO and k-means clustering algorithm
    Younus, Zeyad Safaa
    Mohamad, Dzulkifli
    Saba, Tanzila
    Alkawaz, Mohammed Hazim
    Rehman, Amjad
    Al-Rodhaan, Mznah
    Al-Dhelaan, Abdullah
    ARABIAN JOURNAL OF GEOSCIENCES, 2015, 8 (08) : 6211 - 6224
  • [44] Research on prediction and recommendation of financial stocks based on K-means clustering algorithm optimization
    Fang, Zheng
    Chiao, Chaoshin
    JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2021, 21 (05) : 1081 - 1089
  • [45] Comparison and Detection Analysis of Network Traffic Datasets Using K-Means Clustering Algorithm
    Al-Sanjary, Omar Ismael
    Bin Roslan, Muhammad Aiman
    Helmi, Rabab Alayham Abbas
    Ahmed, Ahmed Abdullah
    JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT, 2020, 19 (03)
  • [46] Based Differential Evolution K-means Algorithm for Fault Clustering on Flight Control System
    Gu Wei
    Zhang Weiguo
    Huang Zhiyi
    Li Lili
    ISTM/2009: 8TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-6, 2009, : 1586 - 1590
  • [47] Research on the spatial distribution of garden landscape based on the optimization of K-means clustering algorithm
    Chen, Yu
    Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)
  • [48] Classification Model for Diabetes Mellitus Diagnosis based on K-Means Clustering Algorithm Optimized with Bat Algorithm
    Anam, Syaiful
    Fitriah, Zuraidah
    Hidayat, Noor
    Maulana, Mochamad Hakim Akbar Assidiq
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (01) : 653 - 659
  • [49] An Investigation of Color Analysis Method for Printed Fabrics Based on K-means Clustering
    Xin, Bin-Jie
    TEXTILE BIOENGINEERING AND INFORMATICS SYMPOSIUM PROCEEDINGS, 2016, VOLS 1 AND 2, 2016, : 912 - 924
  • [50] Customer Segmentation Using K-Means Clustering and the Hybrid Particle Swarm Optimization Algorithm
    Li, Yue
    Qi, Jianfang
    Chu, Xiaoquan
    Mu, Weisong
    COMPUTER JOURNAL, 2023, 66 (04) : 941 - 962