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An intelligent CNC controller using cloud knowledge base

  • Wenbin Gao
  • Chengrui ZhangEmail author
  • Tianliang Hu
  • Yingxin Ye
ORIGINAL ARTICLE
  • 31 Downloads

Abstract

CNC machine tool plays a vital role in intelligent manufacturing, but up to now, CNC controller, which works as the “brain” of the machine tool, is just a loyal executor of machining command without intelligence. This paper is aimed to improve the intelligence of CNC controller from the aspect of machining process planning, which has a great effect on product quality and production efficiency. A STEP-compliant data model is adopted. Compared to previous STEP-NC controller, this paper presents a new paradigm to integrate the ability of autonomous process planning into CNC controller based on cloud knowledge base. A hierarchical and modular architecture is designed to obtain machining process planning from cloud knowledge base timely and to conduct the machining implementation on shop floor. Furthermore, efficient and matching operation mechanism is researched. It offers a proposal to use cloud knowledge to implement intelligent manufacturing. Finally, a case study is demonstrated to verify the feasibility of this intelligent CNC controller.

Keywords

Intelligent CNC controller Cloud knowledge base Machining process planning 

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Notes

Funding information

The work is supported by National Natural Science Foundation of China (Grant No. 51875323) and Science and Technology Plan of Suzhou (Grant No. SYG201709).

References

  1. 1.
    Tao F, Cheng J, Cheng Y, Gu S, Zheng T, Yang H (2017) SDMSim: a manufacturing service supply–demand matching simulator under cloud environment. Robot Comput Integr Manuf 45(6):34–46CrossRefGoogle Scholar
  2. 2.
    Tao F, Zuo Y, Xu LD, Zhang L (2014) IoT-Based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Ind Inf 10(2):1547–1557CrossRefGoogle Scholar
  3. 3.
    Wang J, Ma Y, Zhang L, Gao R, Wu D (2018) Deep learning for smart manufacturing: methods and applications. J Manuf Syst 48:144–156CrossRefGoogle Scholar
  4. 4.
    Ye Y, Hu T, Zhang C, Luo W (2016) Design and development of a CNC machining process knowledge base using cloud technology. Int J Adv Manuf Technol 94(1–13):3413–3425.  https://doi.org/10.1007/s00170-016-9338-1 Google Scholar
  5. 5.
    ISO14649–1 (2002) Industrial automation systems and integration–physical device control–data model for computerized numerical controllers. Part 1: overview and fundamental principles. International Standards Organization (ISO)Google Scholar
  6. 6.
    ISO14649–10 (2002) Industrial automation systems and integration–physical device control–data model for computerized numerical controllers. Part 10: general process data. International Standards Organization (ISO)Google Scholar
  7. 7.
    Xu XW, Newman ST (2006) Making CNC machine tools more open, interoperable and intelligent—a review of the technologies. Comput Ind 57(2):141–152CrossRefGoogle Scholar
  8. 8.
    Wang H, Xu X, Tedford JD (2007) An adaptable CNC system based on STEP-NC and function blocks. Int J Prod Res 45(17):3809–3829CrossRefGoogle Scholar
  9. 9.
    Xu XW (2006) Realization of STEP-NC enabled machining. Robot Comput Integr Manuf 22(2):144–153CrossRefGoogle Scholar
  10. 10.
    Suh SH, Cheon SU (2002) A framework for an intelligent CNC and data model. Int J Adv Manuf Technol 19(10):727–735CrossRefGoogle Scholar
  11. 11.
    Suh SH, Cho JH, Hong HD (2002) On the architecture of intelligent STEP-compliant CNC. Int J Comput Integr Manuf 15(2):168–177CrossRefGoogle Scholar
  12. 12.
    Suh SH, Chung DH, Lee BE, Cho JH, Cheon SU, Hong HD, Lee HS (2002) Developing an integrated STEP-compliant CNC prototype. J Manuf Syst 21(5):350–362CrossRefGoogle Scholar
  13. 13.
    Suh SH, Chung DH, Lee BE, Shin S, Choi I, Kim KM (2006) STEP-compliant CNC system for turning: data model, architecture, and implementation. Comput Aided Des 38(6):677–688CrossRefGoogle Scholar
  14. 14.
    Allen RD, Harding JA, Newman ST (2005) The application of STEP-NC using agent-based process planning. Int J Prod Res 43(4):655–670CrossRefzbMATHGoogle Scholar
  15. 15.
    Nassehi A, Newman ST, Allen RD (2006) The application of multi-agent systems for STEP-NC computer aided process planning of prismatic components. Int J Mach Tool Manu 46(5):559–574CrossRefGoogle Scholar
  16. 16.
    Zhu W, Hu T, Luo W, Yan Y, Zhang C (2018) A STEP-based machining data model for autonomous process generation of intelligent CNC controller. Int J Adv Manuf Technol 96(1–4):271–285.  https://doi.org/10.1007/s00170-017-1554-9 Google Scholar
  17. 17.
    Chang PT, Chang CH (2000) An integrated artificial intelligent computer-aided process planning system. Int J Comput Integr Manuf 13(6):483–497CrossRefGoogle Scholar
  18. 18.
    Deb S, Ghosh K, Paul S (2006) A neural network based methodology for machining operations selection in computer-aided process planning for rotationally symmetrical parts. J Intell Manuf 17(5):557–569CrossRefGoogle Scholar
  19. 19.
    Liu XJ, Yi H, Ni ZH (2013) Application of ant colony optimization algorithm in process planning optimization. J Intell Manuf 24(1):1–13CrossRefGoogle Scholar
  20. 20.
    Li X, Gao L, Shao X (2012) An active learning genetic algorithm for integrated process planning and scheduling. Expert Syst Appl 39(8):6683–6691CrossRefGoogle Scholar
  21. 21.
    Kiritsis D (1995) A review of knowledge-based expert systems for process planning. Methods and problems. Int J Adv Manuf Technol 10(4):240–262CrossRefGoogle Scholar
  22. 22.
    Ye Y, Hu T, Yang Y, Zhu W, Zhang C (2018) A knowledge based intelligent process planning method for controller of computer numerical control machine tools. J Intell Manuf 4:1–17.  https://doi.org/10.1007/s10845-018-1401-3 Google Scholar
  23. 23.
    Tao F, Qi Q (2017) New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Trans Syst Man Cybern Syst 1–11.  https://doi.org/10.1109/TSMC.2017.2723764

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Wenbin Gao
    • 1
    • 2
    • 3
  • Chengrui Zhang
    • 1
    • 2
    Email author
  • Tianliang Hu
    • 1
    • 2
    • 3
  • Yingxin Ye
    • 1
    • 2
  1. 1.School of Mechanical EngineeringShandong UniversityJinanPeople’s Republic of China
  2. 2.Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of EducationShandong UniversityJinanPeople’s Republic of China
  3. 3.Suzhou Institute of Shandong UniversitySuzhouPeople’s Republic of China

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