Abstract
The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, journal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.
Highlights
The technological advances, increases in global competitiveness, diversification of customer requirements, dynamic market trends, digitalization, and constantly increasing process complexities of the 21st century have significantly influenced traditional manufacturing industries [1]
The present study focuses on the possible research gaps and future research potential in implementing digital twin for building a customized DT model in the context of smart manufacturing using bibliometric analysis
The paper covers the various aspects of digital twin, viz., evolution, background, architecture, framework, applications, challenges, benefits, methods of data collection, communication protocols, and decision-making algorithms used for implementing a DT in the domain of smart manufacturing
Summary
The technological advances, increases in global competitiveness, diversification of customer requirements, dynamic market trends, digitalization, and constantly increasing process complexities of the 21st century have significantly influenced traditional manufacturing industries [1]. This divergence drives the traditional manufacturing industries towards smart manufacturing, which integrates the elements (robots, Big Data, cloud computing, Internet-of-Things (IoT), Industrial Internet-of-Things (IIoT), simulations etc.). A lack of maintenance activities leads to sudden equipment failure, unplanned downtime, loss of productivity, and increases in production cost and time [7]. Reactive maintenance leads to sudden equipment failure, unplanned downtime, losses of productivity, and increases in costs of production [13]. To study the different aspects of DT in the context of smart manufacturing, this paper provides a comprehensive bibliometric analysis of DT in the domain of smart manufacturing, and studies the benefits, applications, and challenges in implementing a DT in real-time
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