Trends in intelligent manufacturing research: a keyword co-occurrence network based review

Chenxi Yuan (), Guoyan Li (), Sagar Kamarthi (), Xiaoning Jin () and Mohsen Moghaddam ()
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Chenxi Yuan: Northeastern University
Guoyan Li: Northeastern University
Sagar Kamarthi: Northeastern University
Xiaoning Jin: Northeastern University
Mohsen Moghaddam: Northeastern University

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 2, No 3, 425-439

Abstract: Abstract In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber manufacturing. Using a network science and data mining-based Keyword Co-occurrence Network (KCN) methodology, this work analyzes the trends in data science topics in the manufacturing literature over the past two decades to inform the researchers, educators, industry leaders of knowledge trends in intelligent manufacturing. It studies the evolution of research topics and methods in data science, Internet of Things (IoT), cloud computing, and cyber manufacturing. The KCN methodology is applied to systematically analyze the keywords collected from 84,041 papers published in top-tier manufacturing journals between 2000 and 2020. It is not practically feasible to review this large body of literature through tradition manual approaches like systematic review and scoping review to discover insights. The results of network modeling and data analysis reveal important knowledge components and structure of the intelligent and cyber manufacturing literature, implicit the research interests switch and provide the insights for industry development. This paper maps the high frequency keywords in the recent literature to nine pillars of Industry 4.0 to help manufacturing community identify research and education directions for emerging technologies in intelligent manufacturing.

Keywords: Keyword co-occurrence network; Industry 4.0; Intelligent manufacturing; Data science; Machine learning; Artificial intelligence (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10845-021-01885-x

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