IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v27y2016i3d10.1007_s10845-014-0887-6.html
   My bibliography  Save this article

A weighted-coupled network-based quality control method for improving key features in product manufacturing process

Author

Listed:
  • Guangzhou Diao

    (Xi’an Jiaotong University)

  • Liping Zhao

    (Xi’an Jiaotong University)

  • Yiyong Yao

    (Xi’an Jiaotong University)

Abstract

There are some complicated coupling relations among quality features (QFs) in manufacturing process. Generally, the machining errors of one key feature may cause some errors of other features which are coupled with the key one. Considering the roles of key QFs, the weighted-coupled network-based quality control method for improving key features is proposed in this paper. Firstly, the W-CN model is established by defining the mapping rules of network elements (i.e. node, edge, weight). Secondly, some performance indices are introduced to evaluate the properties of W-CN. The influence index of node is calculated to identify the key nodes representing key features. Thirdly, three coupling modes of nodes are discussed and coupling degrees of key nodes are calculated to describe the coupling strengthen. Then, the decoupling method based on small world optimization algorithm is discussed to analyze the status changes of key nodes accurately. Finally, a case of engine cylinder body is presented to illustrate and verify the proposed method. The results show that the method is able to provide guidance for improving product quality in manufacturing process

Suggested Citation

  • Guangzhou Diao & Liping Zhao & Yiyong Yao, 2016. "A weighted-coupled network-based quality control method for improving key features in product manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 535-548, June.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:3:d:10.1007_s10845-014-0887-6
    DOI: 10.1007/s10845-014-0887-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-014-0887-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-014-0887-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Capocci, A. & Servedio, V.D.P. & Caldarelli, G. & Colaiori, F., 2005. "Detecting communities in large networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(2), pages 669-676.
    2. Ming Jin & Yanting Li & Fugee Tsung, 2010. "Chart allocation strategy for serial-parallel multistage manufacturing processes," IISE Transactions, Taylor & Francis Journals, vol. 42(8), pages 577-588.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fuqiang Zhao & Lichao Zhang & Guijun Yang & Li He & Fengyu Yan, 2017. "Application Of Cut Algorithm Based On Algebraic Connectivity To Community Detection," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 20(01), pages 1-18, February.
    2. Chen, Lei & Kou, Yingxin & Li, Zhanwu & Xu, An & Wu, Cheng, 2018. "Empirical research on complex networks modeling of combat SoS based on data from real war-game, Part I: Statistical characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 754-773.
    3. Cheng, Guoqing & Li, Ling, 2020. "Joint optimization of production, quality control and maintenance for serial-parallel multistage production systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Li, Jianyu & Zhou, Jie, 2007. "Chinese character structure analysis based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 629-638.
    5. Tugrul Temel & Paul Phumpiu, 2021. "Pathways to recovery from COVID-19: characterizing input–output linkages of a targeted sector," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 10(1), pages 1-24, December.
    6. repec:ctc:serie1:def14 is not listed on IDEAS
    7. Yang, Bo & Li, Xu & Liu, Xiangwei & He, He & Chen, Wei, 2019. "Alternating between consensus and leader selection reveals community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 693-706.
    8. Lou, Hao & Li, Shenghong & Zhao, Yuxin, 2013. "Detecting community structure using label propagation with weighted coherent neighborhood propinquity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3095-3105.
    9. Zhang, Dawei & Xie, Fuding & Zhang, Yong & Dong, Fangyan & Hirota, Kaoru, 2010. "Fuzzy analysis of community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5319-5327.
    10. Shen, Yi & Pei, Wenjiang & Wang, Kai & Li, Tao & Wang, Shaoping, 2008. "Recursive filtration method for detecting community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6663-6670.
    11. Luthi, Leslie & Pestelacci, Enea & Tomassini, Marco, 2008. "Cooperation and community structure in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 955-966.
    12. Jiao, Qing-Ju & Huang, Yan & Shen, Hong-Bin, 2015. "Community mining with new node similarity by incorporating both global and local topological knowledge in a constrained random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 363-371.
    13. Byungyun Yang & Minjun Kim & Changkyu Lee & Suyeon Hwang & Jinmu Choi, 2022. "Developing an Automated Analytical Process for Disaster Response and Recovery in Communities Prone to Isolation," IJERPH, MDPI, vol. 19(21), pages 1-19, October.
    14. Pecora, Nicolò & Spelta, Alessandro, 2015. "Shareholding relationships in the Euro Area banking market: A network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 1-12.
    15. Nicolò Pecora & Alessandro Spelta, 2014. "Shareholding Network in the Euro Area Banking Market," DISCE - Working Papers del Dipartimento di Economia e Finanza def014, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    16. Li, Zhangtao & Liu, Jing, 2016. "A multi-agent genetic algorithm for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 336-347.
    17. Chen, Kaiqi & Bi, Weihong, 2019. "A new genetic algorithm for community detection using matrix representation method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    18. Li, Jianyu & Zhou, Jie & Luo, Xiaoyue & Yang, Zhanxin, 2012. "Chinese lexical networks: The structure, function and formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5254-5263.
    19. Yu, Jia-Wei & Xie, Wen-Jie & Jiang, Zhi-Qiang, 2018. "Early warning model based on correlated networks in global crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1335-1343.
    20. Igor Mezić & Vladimir A. Fonoberov & Maria Fonoberova & Tuhin Sahai, 2019. "Spectral Complexity of Directed Graphs and Application to Structural Decomposition," Complexity, Hindawi, vol. 2019, pages 1-18, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:27:y:2016:i:3:d:10.1007_s10845-014-0887-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.