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Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions

Author

Listed:
  • Muhammad Asif

    (CNC System Research and Development Division, China Academy of Machinery Ningbo Academy of Intelligent Machine Tool Co., Ltd., Ningbo 315000, China)

  • Hang Shen

    (CNC System Research and Development Division, China Academy of Machinery Ningbo Academy of Intelligent Machine Tool Co., Ltd., Ningbo 315000, China)

  • Chunlin Zhou

    (CNC System Research and Development Division, China Academy of Machinery Ningbo Academy of Intelligent Machine Tool Co., Ltd., Ningbo 315000, China)

  • Yuandong Guo

    (CNC System Research and Development Division, China Academy of Machinery Ningbo Academy of Intelligent Machine Tool Co., Ltd., Ningbo 315000, China)

  • Yibo Yuan

    (CNC System Research and Development Division, China Academy of Machinery Ningbo Academy of Intelligent Machine Tool Co., Ltd., Ningbo 315000, China)

  • Pu Shao

    (CNC System Research and Development Division, China Academy of Machinery Ningbo Academy of Intelligent Machine Tool Co., Ltd., Ningbo 315000, China)

  • Lan Xie

    (CNC System Research and Development Division, China Academy of Machinery Ningbo Academy of Intelligent Machine Tool Co., Ltd., Ningbo 315000, China)

  • Muhammad Shoaib Bhutta

    (School of Automotive and Transportation Engineering, Guilin University of Aerospace Technology, Guilin 541004, China)

Abstract

Intelligent manufacturing is considered among the most important elements of the modern industrial revolution, which includes digitalization, networking, and the development of the intelligent manufacturing industry. With the progressive development of modern information technology, particularly the new generation of artificial intelligence (AI) technology, many new opportunities are coming into existence for intelligent machine tool (IMT) development. Intelligent machine tools offer diverse advantages, including learning and optimizing machining processes, error compensation, energy savings, and failure prevention. The paper focuses on the machine tool market in terms of global production, the leading machine tool-producing countries, and the leading countries’ market share in machine tool production. Moreover, the usage of various artificial intelligence techniques in intelligent machining operations is also considered in this comprehensive review, including machining parameter optimization, tool condition monitoring (TCM), and chatter vibration management of intelligent machine tools. Furthermore, future challenges for the machine tool industry are also highlighted.

Suggested Citation

  • Muhammad Asif & Hang Shen & Chunlin Zhou & Yuandong Guo & Yibo Yuan & Pu Shao & Lan Xie & Muhammad Shoaib Bhutta, 2023. "Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions," Sustainability, MDPI, vol. 15(10), pages 1-28, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8298-:d:1150992
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    References listed on IDEAS

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    1. Zoran Jurkovic & Goran Cukor & Miran Brezocnik & Tomislav Brajkovic, 2018. "A comparison of machine learning methods for cutting parameters prediction in high speed turning process," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1683-1693, December.
    2. Chen, Xingzheng & Li, Congbo & Tang, Ying & Li, Li & Du, Yanbin & Li, Lingling, 2019. "Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time," Energy, Elsevier, vol. 175(C), pages 1021-1037.
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