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Review of Lubrication and Cooling in Computer Numerical Control (CNC) Machine Tools: A Content and Visualization Analysis, Research Hotspots and Gaps

Author

Listed:
  • Raman Kumar

    (Department of Mechanical and Production Engineering, Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India)

  • Shubham Sharma

    (Mechanical Engineering Department, University Centre for Research and Development, Chandigarh University, Mohali 140413, Punjab, India
    School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Ranvijay Kumar

    (Mechanical Engineering Department, University Centre for Research and Development, Chandigarh University, Mohali 140413, Punjab, India)

  • Sanjeev Verma

    (Mechanical Engineering Department, Chitkara University Institute of Engineering and Technology, Chitkara University, NH-64 Village Jansla, Rajpura 140401, Punjab, India)

  • Mohammad Rafighi

    (Department of Aeronautical Engineering, Sivas University of Science and Technology, Sivas 58000, Türkiye)

Abstract

Lubrication and cooling (LC) are critical for mechanical devices’ effective and dependable functioning, because they decrease friction and wear of moving components, ensuring superior efficiency. However, the cutting fluids in machining operations are a key cause of fear, due to their high cost, environmental impact, and health risks, particularly in computer numerical control (CNC) machine tools (MTs). During the industrial revolutions, MTs superseded manual labour and increased efficiency and output. Therefore, much research was conducted on lubrication and cooling in CNC machine tools (LC in CNC MTs). Therefore, it has become necessary to review and highlight research hotspots and gaps using specific means that can benefit budding researchers. The present review aims to identify research hotspots and gaps of LC in CNC MTs utilizing content and visualization analysis, employing VOSviewer and Biblioshiny software. The analysis comprises 136 documents retrieved by Scopus between 1988 and 2022. The analysis revealed a consistent growth in publications, primarily consisting of articles, with a minor proportion of review documents and conference papers. The keywords were categorized into seven clusters, with a notable prevalence of ‘surface roughness’ and ‘CNC machine tools’. A word cloud was generated to visualize the author’s frequently used keywords, where larger font sizes represented higher frequency. The treemaps demonstrated that ‘CNC’ appeared 34 times and contributed 8%, followed by ‘machine’, ‘tool’, ‘machining’, and ‘thermal’. In the abstract-terms tree plot, ‘machine’ appeared 235 times and contributed 7%, followed by ‘CNC’, ‘machining’, ‘tool’, and ‘cutting’. The content and visualization analysis identified six research hotspots: computer control systems, machine tools, computer numerical control, machining, numerical control systems, and surface roughness (Ra). The research gaps are temperature, cooling systems, cutting forces, energy utilization, tool life, nanoparticles, electric power utilization, and energy conservation. Based on hotspots and gaps, literature evaluations extensively addressed the strong roadmap of technical improvements and problems of LC in CNC MTs. A complete visualization and content analysis also produced a conceptual framework for best practices, and the study offers insight into the issues and prospects.

Suggested Citation

  • Raman Kumar & Shubham Sharma & Ranvijay Kumar & Sanjeev Verma & Mohammad Rafighi, 2023. "Review of Lubrication and Cooling in Computer Numerical Control (CNC) Machine Tools: A Content and Visualization Analysis, Research Hotspots and Gaps," Sustainability, MDPI, vol. 15(6), pages 1-44, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4970-:d:1093887
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    References listed on IDEAS

    as
    1. Ardamanbir Singh Sidhu & Sehijpal Singh & Raman Kumar & Danil Yurievich Pimenov & Khaled Giasin, 2021. "Prioritizing Energy-Intensive Machining Operations and Gauging the Influence of Electric Parameters: An Industrial Case Study," Energies, MDPI, vol. 14(16), pages 1-39, August.
    2. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    3. Raman Kumar & Sehijpal Singh & Ardamanbir Singh Sidhu & Catalin I. Pruncu, 2021. "Bibliometric Analysis of Specific Energy Consumption (SEC) in Machining Operations: A Sustainable Response," Sustainability, MDPI, vol. 13(10), pages 1-30, May.
    4. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    5. Li, He & Deng, Zhi-Ming & Golilarz, Noorbakhsh Amiri & Guedes Soares, C., 2021. "Reliability analysis of the main drive system of a CNC machine tool including early failures," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    6. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    7. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    8. Navarro Ferronato & Vincenzo Torretta, 2019. "Waste Mismanagement in Developing Countries: A Review of Global Issues," IJERPH, MDPI, vol. 16(6), pages 1-28, March.
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    1. Bartłomiej Krawczyk & Piotr Szablewski & Bartosz Gapiński & Michał Wieczorowski & Rehan Khan, 2024. "On-Machine Measurement as a Factor Affecting the Sustainability of the Machining Process," Sustainability, MDPI, vol. 16(5), pages 1-15, March.

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