Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

Vivek Warke, Satish Kumar, Arunkumar Bongale and Ketan Kotecha
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Vivek Warke: Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, India
Satish Kumar: Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, India
Arunkumar Bongale: Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, India
Ketan Kotecha: Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, India

Sustainability, 2021, vol. 13, issue 18, 1-49

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.

Keywords: digital twin; industry 4.0; Proknow-C; artificial intelligence; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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