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A knowledge based intelligent process planning method for controller of computer numerical control machine tools

Author

Listed:
  • Yingxin Ye

    (Shandong University
    Ministry of Education)

  • Tianliang Hu

    (Shandong University
    Ministry of Education)

  • Yan Yang

    (Shandong University
    Ministry of Education)

  • Wendan Zhu

    (Shandong University
    Ministry of Education)

  • Chengrui Zhang

    (Shandong University
    Ministry of Education)

Abstract

The development of computer, internet and information technology puts forward higher demands for Computer Numerical Control (CNC) machine tools to improve the intelligence in many aspects. Among these aspects, intelligent process planning plays an important role in current changeable market and customized product promotion by shortening production cycle and providing more stable process planning ability. To realize intelligent process planning, a CNC controller with cloud knowledge base support is proposed with ability of making process planning autonomously based on workpiece design. Previous work of knowledge model and cloud knowledge base framework design is introduced, and then this paper focuses on the complete process planning method within the intelligent CNC controller. Both interactivity between knowledge base and CNC controller, and query/infer mechanism in knowledge base are illustrated in detail. A case study of two shafts process planning is shown to demonstrate the feasibility of the intelligent process planning method.

Suggested Citation

  • Yingxin Ye & Tianliang Hu & Yan Yang & Wendan Zhu & Chengrui Zhang, 2020. "A knowledge based intelligent process planning method for controller of computer numerical control machine tools," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1751-1767, October.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:7:d:10.1007_s10845-018-1401-3
    DOI: 10.1007/s10845-018-1401-3
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    References listed on IDEAS

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    1. Tao, Fei & Zhao, Dongming & Yefa, Hu & Zhou, Zude, 2010. "Correlation-aware resource service composition and optimal-selection in manufacturing grid," European Journal of Operational Research, Elsevier, vol. 201(1), pages 129-143, February.
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    Cited by:

    1. Kai Zhang & Zhiying Tu & Dianhui Chu & Xiaoping Lu & Lucheng Chen, 2024. "Aic: an industrial knowledge graph with Abstraction-Instance-Capability reasoning abilities for personalized customization," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3419-3440, October.
    2. Antoni Świć & Arkadiusz Gola & Łukasz Sobaszek & Natalia Šmidová, 2021. "A thermo-mechanical machining method for improving the accuracy and stability of the geometric shape of long low-rigidity shafts," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1939-1951, October.
    3. Qihao Liu & Xinyu Li & Liang Gao, 2021. "Mathematical modeling and a hybrid evolutionary algorithm for process planning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 781-797, March.
    4. Radi Romansky, 2021. "Mathematical Modelling and Study of Stochastic Parameters of Computer Data Processing," Mathematics, MDPI, vol. 9(18), pages 1-14, September.
    5. Roman Stryczek & Kamil Wyrobek, 2021. "Heuristic techniques for modelling machine spinning processes," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1189-1206, April.
    6. Rui Wang & Xiangyu Guo & Shisheng Zhong & Gaolei Peng & Lin Wang, 2022. "Decision rule mining for machining method chains based on rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 799-807, March.

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