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

The fourth industrial revolution (or Industry 4.0) is driven by the emergence of new technologies such as cyber-physical systems and Internet of Things (IoT) that focus heavily on interconnectivity, automation, machine learning, and real-time analytics. As a result, several paradigms have emerged in recent years to characterize the architectures and requirements for next-generation manufacturing systems:

Despite the differences between the objectives of the intelligent manufacturing facets mentioned above, their common vernacular is to enable (Moghaddam et al., 2018): (1) integrated and collaborative manufacturing systems and value networks connected via IoT, (2) digitalization and integration of manufacturing resources on the cloud as secure and on-demand services, and (3) connected, intelligent, and autonomous cyber-physical machines enabled by cloud/edge computing and machine learning technologies (Tian et al., 2002). While different segments or domains of the manufacturing industry may have different needs, they are all expected to leverage connectedness and access to real-time insights across systems, processes, partners, products, and people. The goal is to enable mass-customization of products and services; adopt automatic and flexible production lines (Escobar et al., 2021); track parts and products in real-time; facilitate communication among parts, products, and machines; enhance human-machine collaboration(Nguyen Ngoc et al., 2021); achieve IoT-enabled production optimization in smart factories (Yao et al., 2019); create new types of services and business models of interaction in value chains (Alcácer & Cruz-Machado, 2019); achieve a higher level of intelligent automation; and ultimately increase quality, productivity, flexibility, lower costs, and higher efficiency (Barari et al., 2021).

Figure 1 illustrates the convergence of various cyber-physical manufacturing technologies that collectively aim at materializing the vision of future manufacturing described above. This convergence, in turn, is leading to the development of new knowledge and methods at the intersection of the physical world technologies (e.g., robotics, automation, product-service systems, cloud manufacturing) and the cyber world technologies (e.g., AI, machine learning, multi-agent systems, cloud computing). As the emerging cyber-physical manufacturing technologies grow more complex and more knowledge-intensive, the workforce is expected to be adept at handling these technologies. For example, big data analytics and management (e.g., data mining, data classification, data storage) are becoming a critical challenge for manufacturers (Choudhary et al., 2009) (Yao et al., 2019). New cloud architectures and services are needed for analyzing data under certain security and privacy protocols(Alam & Saddik, 2017). New machine learning algorithms and cloud services are required for optimizing manufacturing processes and systems (Usuga Cadavid et al., 2020). The rise of deep learning poses new opportunities and challenges for manufacturers to decode the increasingly complex manufacturing processes and large-scale production systems to be competitive in their rapidly changing industry (Miškuf & Zolotová, 2016; Oztemel & Gursev, 2020). In this context, both researchers and practitioners need to identify emerging research trends, technologies, and workforce knowledge requirements. This is the focal issue investigated in this paper.

This paper is motivated by an urgent need for observing the research trends in rapidly changing interelligent manufacturing landscape(Radhakrishnan et al., 2017). We conducted a systematic review of 84,041 recent articles published in leading journals ranked Q1 by the Scimago Journal & Country Rank (SJR). 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. A KCN is created by treating the keywords of the articles as individual nodes and each co-occurrence of a pair of keywords is modeled as a link between their respective nodes. The co-occurrence frequency of each pair of keywords is represented as the weight of the link connecting the pair. The proposed KCN methodology is implemented on the keywords listed in the 84,041 articles to obtain key topological parameters and perform a statistical and visual analysis of the network to reveal potential patterns and evolution of keywords, providing insight into the emerging knowledge trends in the manufacturing sector.

The remainder of this article is organized as follows. Background Section discusses the background and related work on the identification of emerging knowledge trends and skills requirements in the manufacturing industry. The KCN Methodology Section introduces the KCN methodology and elaborates on the design of experiments. Results and Analyses Section presents the results and analyses of the experiments. Discussion Section discusses the implications of the results for identifying key knowledge trends and workforce skills requirements in future manufacturing systems. Conclusions Section concludes the article and summarizes the findings and directions for future research.

Two main network-based methods have been widely used for the review of scientific and technical publications (Li et al., 2016). The first is the citation network, which identifies important academic articles according to citation frequency, and focuses on studying the structure of scientific communication by analyzing links between citations in the literature during a short period (Onel et al., 2011) (Shibata et al., 2011). This method is not suitable for the purpose of current work, because it focuses on the relative impact of published scientific work rather than on the emerging technology trends. The other frequently used method is keyword network analysis (KCN) (Su & Lee, 2010), which explores the links between keywords in the literature to reveal the knowledge components and structure of a scientific field. Keywords can provide a concise overview of the important content and key points of a body of articles as an essential textual element (Li et al., 2016). The KCN methodology captures the connections between different concepts at a micro-level and provides insights about respective roles and importance. Further, the KCN methodology can deliver network attributes on articles published over a long period and hence reveal the evolution of the topics over time (Zhong et al., 2014). Therefore, this paper applies the concept of KCN to analyze the linkage of concepts and investigate the research topics, their relationships, and development trends in manufacturing science and engineering. The rationale behind the KCN methodology is that the keywords of top-tier, peer-reviewed journal articles represent the most important areas of research and development in their respective area of study and that their co-occurrence represents their relationships and relative importance. This technique can analyze the content of a large number of papers by quantifying the associations between keywords, reflecting on the respective role and evolution of keywords, and identify the overall structure of the research field (Lozano et al., 2019).

To build the KCN for manufacturing science and engineering, we collected the keywords of top-tier journals published in the period betwen2000 and 2019 in 43 journals that are ranked as SJR Q1 (Table 1). All papers published in each journal were accessed through the Web of Science database and the author-specified keywords from all these articles were collected for further processing. A total of 84,041 articles were identified, which collectively comprise a total of 258,882 standard keywords defined by their authors (Table 2). The number of publications increased from 1638 papers in 2,000 to 7,887 papers in 2019. These keywords were reconciled to eliminate redundancy, by unifying singular and plural variants (e.g., “algorithms” and “algorithm”), hyphened and non-hyphenated phrases (e.g., “multi agent system” and “multi-agent system”), synonyms (e.g., “statistical methods” and “statistical modeling”), and acronym variants (e.g., “artificial neural networks (ANN)”, “artificial neural networks”, and “ANN”). Next, keywords relevant to the cyber-world and physical-world manufacturing science, engineering, and technology were selected by three authors knowledgeable about these areas. The selection of keywords by three authors was compiled into the final keyword list with 178,111 keywords in total. Through this process, the keywords that had occurred with high frequency but were irrelevant to cyber-physical manufacturing technologies were eliminated. Co-occurrence of all pairs of keywords was then calculated by counting the co-appearance of word-pairs in all the papers. The procedure of data collection, processing and cleaning are described in Fig. 2.

The papers published between 2000 and 2019 were partitioned into four 5-year windows: 2000–2004, 2005–2009, 2010–2014, and 2015–2019. A separate KCN is constructed for each window to explore the temporal evolution of the trends in manufacturing science and engineering research. The attributes of the KCNs are then imported into the Network Workbench software (NWB Team, 2006) to retrieve various network parameters to explore co-occurrence patterns among keywords, and then generate insights on the potential knowledge structure of the emerging trends in manufacturing.

This section describes the network analysis parameters (Radhakrishnan et al., 2017) used in the KCN methodology for analyzing the network connection weights and the relationship between different nodes. The first parameter is degree, which is the total number of links that connect a given node to other nodes in a network. The degree of node \(i\) represents its relative importance in the network, as follows:where \(N\) denotes the set of all keywords in the keyword network, and \(e_{ij} \in \left\{ {0,1} \right\}\) denotes the existence of an edge between nodes \(i\) and \(j\) (\(i,j \in N\)). In the KCN methodology, the higher the degree of a node, the greater the centrality of the node in the keyword network. Centrality of a keyword is an indicator of its importance.

The keyword network is a weighted network. The weight \(w_{ij}\) of an edge \(e_{ij}\) represents the count of nodes \(i\) and \(j\) co-occurring. The strength of node \(i\) is the sum of the weights of all edges connected to node \(i\). It is, like centrality, an indicator of the relative importance and connectivity of a keyword in the network. The strength of node \(i\) is calculated as follow:

Another key parameter for KCN analysis is the average weight of end point degrees, which measures the co-occurrence of the edges between pairs of nodes as the degrees of the nodes change. It is defined as follows:where \(k_{i}\) and \(k_{j}\) are degrees of nodes \(i\) and \(j\), respectively, and \(\left\langle \cdot \right\rangle\) denotes average value. In the context of this research, the average weight of end point degrees answers the following question: Are the connections between high-degree keywords stronger than of the connections between low-degree keywords? This parameter can be approximated by a power-law:where the exponent \(\theta\) is a positive constant. Positive correlation confirms that highly connected keywords are more likely to have stronger connections.

The next network parameter examined in this study is the average weighted nearest neighbor's degree of node \(i\), for all \(i \in N\). It measures the tendency of a node to link with its neighbors with similar degree characteristics: high degree nodes connecting with high degree neighbors and low degree nodes connecting with low degree neighbors. This parameter can be calculated as follows:

If this value increases with the keyword degrees, it represents the following assortative behavior: high-degree keywords are associated with other high-degree keywords in the papers. Inversely, the disassortative behavior indicates that high-degree keywords co-occur with low-degree keywords.

The final parameter is the weighted clustering coefficient, which measures the local cohesiveness of node \(i\) among its neighbors. It represents the importance of the structure clustered around a certain keyword on the basis of the interaction intensity found on the local triplets (Barrat et al., 2004). This parameter is formulated as follows:

A greater c_i indicates that keyword i has better connectivity and cohesiveness to other keywords.