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Early identification of technological convergence in numerical control machine tool: a deep learning approach

Author

Listed:
  • Dejing Kong

    (Beijing University of Posts and Telecommunications)

  • Jianzhong Yang

    (Huazhong University of Science and Technology)

  • Lingfeng Li

    (Huazhong University of Science and Technology)

Abstract

The importance of technology convergence of multidisciplinary knowledge has increased recently, and it is a crucial way to spur emerging technologies. Therefore, to understand and identify the technology convergence, which refers to the combination of two or more technological elements for a new system with new functions, is an important issue for both the researchers and the company directors. To identify and investigate the patterns of technology convergence, this research examines the numerical control machine tool, which has typical characteristics of technology convergence in recent years. Based on the numerical control machine tool related publications published between 1997 and 2019, we perform a deep learning approach based on Graph Neural Network model using publication citation network topology and text information together, to identify the technology convergence trajectory and to examine the dynamic role of corresponding technology sub-fields in the technology convergence. The results show that there was an obvious increase for the interdisciplinary citations from information technology to NC machine tool in recent years, and the technology convergence on NC machine tool is signal processing in machining and application of intelligent algorithms in motion control and process planning. In addition, the revelation of the technology convergence early identification contributes to the formation theory of emerging technologies that are interdisciplinary, and is of great interest to researchers, policy makers, and industrialists.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:3:d:10.1007_s11192-020-03696-y
    DOI: 10.1007/s11192-020-03696-y
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