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.
Highlights
The manufacturing industry has always been significantly influenced by technological revolutions—from the invention of steam engines and electricity to the rise of robotics and automation, the Internet of things (IoT), and cyber-physical systems
The main objective of this paper is to identified the knowledge components, knowledge structure, and research trends using Keyword Co-occurrence Network (KCN) approach (Duvvuru & Kamarthi, 2012; Radhakrishnan et al, 2017), which would allow us to explore and analyze a vast amount of literature covering 84,041 papers published in top-tier manufacturing journals between 2000 and 2020
A Keyword Co-occurrence Network (KCN) methodology (Duvvuru & Kamarthi, 2012; Radhakrishnan et al, 2017) to identify knowledge components, knowledge structure, and research trends associated with emerging cyber-physical manufacturing technology areas including the items listed in Fig. 1 and beyond
Summary
The manufacturing industry has always been significantly influenced by technological revolutions—from the invention of steam engines and electricity to the rise of robotics and automation, the Internet of things (IoT), and cyber-physical systems. The scientific and technological advancements (Moghaddam et al, 2018) have enabled emerging paradigms, such as smart manufacturing (Kusiak, 2018; Lu et al, 2016), cyber-physical production systems (Monostori et al, 2016), Industry 4.0 (Oztemel & Gursev, 2020), and cloud-enabled manufacturing (Chen, 2017). We present a review of existing studies that investigate various aspects of technologies in cyber-physical manufacturing in a systematic fashion. DeFelice and Petrillo (2018) conduct a literature review on smart manufacturing using a multicriteria decision model They identify cyber-physical systems, IoT, and big data as the most frequently used terms in smart manufacturing based on a large number of publications in years 2014–2016. They anticipate IoT to play a major role in manufacturing
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