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Exploring the Development of Research, Technology and Business of Machine Tool Domain in New-Generation Information Technology Environment Based on Machine Learning

Author

Listed:
  • Jihong Chen

    (National Numerical Control Systems Engineering Research Center, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Kai Zhang

    (National Numerical Control Systems Engineering Research Center, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yuan Zhou

    (School of Public Policy and Management, Tsinghua University, Beijing 100084, China)

  • Yufei Liu

    (The CAE Center for Strategic Studies, Chinese Academy of Engineering, Beijing 100088, China)

  • Lingfeng Li

    (National Numerical Control Systems Engineering Research Center, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zheng Chen

    (National Numerical Control Systems Engineering Research Center, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Li Yin

    (National Numerical Control Systems Engineering Research Center, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

The combination of new-generation information technology and manufacturing technology has had in a significant and profound impact on the future development paradigm of manufacturing. Machine tools are the basis of virtually everything that is manufactured in the industry, exploring the development of the machine tool domain is of considerable significance to identify the opportunity to develop manufacturing industry and promote the sustainable development of manufacturing in the current tightening constraints of resource environment. Although much attention has been paid to development studies of a specific domain in recent years, it is challenging to conduct a multidimensional study related to the development status of the machine tool domain using existing methods. To solve this challenge, we propose an integrating framework combining topic models, bibliometric, trend analysis and patent analysis to mine multi-source literature within the machine tool domain, including papers, funds, patents, and news. Specifically, papers and funds provided two different perspectives to explore the development status in the research of machine tools. Furthermore, the technology development of machine tools was investigated through patents analysis. Finally, news related to the machine tool industry in recent years was analyzed to examine business focuses on machine tools. The integration of above various analytical methods and multi-dimensional mining of literature enabled analyzing the development of the machine tool domain systematically from multi-perspectives that include research, technology development and industry to provide inspirations about the implications of sustainable development of this domain. The conclusions obtained in this paper is beneficial to different communities of machine tools, in terms of determining the research directions for researchers, identifying industry opportunities for corporations and developing reasonable industry policy for policy makers.

Suggested Citation

  • Jihong Chen & Kai Zhang & Yuan Zhou & Yufei Liu & Lingfeng Li & Zheng Chen & Li Yin, 2019. "Exploring the Development of Research, Technology and Business of Machine Tool Domain in New-Generation Information Technology Environment Based on Machine Learning," Sustainability, MDPI, vol. 11(12), pages 1-38, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3316-:d:240153
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    References listed on IDEAS

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    Cited by:

    1. MARINCEAN Dan Andrei, 2020. "Digital Economy And The Dsm," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(2), pages 86-97, December.
    2. Taeyeoun Roh & Yujin Jeong & Hyejin Jang & Byungun Yoon, 2019. "Technology opportunity discovery by structuring user needs based on natural language processing and machine learning," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-27, October.
    3. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    4. Arelys López-Concepción & Ana Gil-Lacruz & Isabel Saz-Gil & Víctor Bazán-Monasterio, 2022. "Social Well-Being for a Sustainable Future: The Influence of Trust in Big Business and Banks on Perceptions of Technological Development from a Life Satisfaction Perspective in Latin America," Sustainability, MDPI, vol. 15(1), pages 1-14, December.

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