Assessment of researches and case studies on Cloud Manufacturing: a bibliometric analysis

Abstract

Cloud computing technology has been studied in the context of industry 4.0 as a tool applied to manufacturing services and resources. Such concept is widely known as Cloud Manufacturing. This paper aims at mapping the current state of academic researches on this field, promoting the understanding of trends, references and practical applications in real-life conditions. A bibliometric analysis was conducted using two different databases -Scopus and Web of Sciences -and VOSviewer's text mining tools and techniques. From a sample of 1,420 papers, this study identified the countries which had the largest volume of publications, the main journals related to the subject, the most influent articles, and four clusters by keywords occurrences: (i) "Optimization of manufacturing processes", (ii) "Collaborative networks of manufacturing resources and services", (iii) "Industry 4.0 and cloud computing systems", and (iv) "Data reliability and cyber-security". Finally, this work selected and analyzed the 159 articles with applied case studies, in order to stratify and to understand the most common approaches within the four pre-established categories. This article can contribute to researchers and developers searching for successful practical applications in digitalization of manufacturing chains, as well as to those who are looking for gaps in the still unexplored fields of Cloud Manufacturing. Both the assessment and the categorization of the case studies about Cloud Manufacturing is a differential in this article.

Latency mitigation using applied HMMs for mixed reality-enhanced intuitive teleoperation in intelligent robotic welding

“…A Gaussian distribution was established for the generative probabilistic model behind the HMM, and the iteration number and hidden state number were solved as parameters. A penalization approach was applied to choose the optimal number of hidden states and iterations of training, using the Bayesian information criterion (BIC) [59,64], which selects the model minimizing the Bayesian posterior probability, defined, M * BIC : where the previous model M i is modified by i , a parameter vector with a length K i and N observations, * i , the maximum likelihood estimator of i and log likelihood L M i .…”

“…The incorporation of the cloud network, not covered indepth in this paper, makes the system easy to implement, as all processing may be performed using cloud computing [64]. Additionally, this system is kept low cost, as it does not require expensive, dedicated equipment or communication lines.…”

Research on the knowledge graph based life cycle carbon footprint labeling representation model for electromechanical products

“…VOSviewer software was used to analyze and retrieve the keywords of "industrial-level knowledge graph" in the WOS database for the period of February 2022, and to cluster and analyze more than 700 documents related to it in the last 10 years, Figure 2 reveals a keyword co-occurrence graph in which the keywords of the authors and other related terms often used in titles and abstracts are taken into account, and each keyword in the graph is represented by a circle , the size of the circle is proportional to its frequency of occurrence, the position indicates the relationship between two different keywords and the line indicates the connection and association of concepts [7] . The data analysis process using VOSviewer software is mainly divided into 3 steps.…”

Exploring the barriers and motivators of value co-creation through a theoretical lens of service-dominant logic

“…There is a growing interest in assessing the current state of academic studies. The bibliometric analysis method was used in many studies of service research, including ecosystem services evaluation (Chen et al, 2020), cloud manufacturing (Morelli and Ignacio, 2021), service innovation research (Sakata et al, 2013), smart cities research (Guo et al, 2019), research on goods and service tax (Dhar and Khandelwal, 2020), innovation metrics (Ferasso and Cherobim, 2017), service supply chain (Nagariya et al, 2019) and knowledge development in thoughtful tourism research (Johnson and Samakovlis, 2019). They all add essential pieces to service research.…”