Digital twins in manufacturing: systematic literature review for physical-digital layer categorization and future research directions

Atalay M., Murat U., Oksuz B., Parlaktuna A. M., PİŞİRİR E., TESTİK M. C.

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, vol.35, no.7, pp.679-705, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Review
  • Volume: 35 Issue: 7
  • Publication Date: 2022
  • Doi Number: 10.1080/0951192x.2021.2022762
  • Journal Name: INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, INSPEC
  • Page Numbers: pp.679-705
  • Keywords: Digital twin, digital-physical layer interaction, manufacturing systems, fourth industrial revolution, systematic literature review, Industry 4, 0, industrial artificial intelligence, digital transformation, PRODUCT LIFE-CYCLE, INDUSTRY 4.0, ENABLING TECHNOLOGIES, CO-SIMULATION, MACHINE-TOOLS, SHOP-FLOOR, DESIGN, FRAMEWORK, SERVICE, INTEGRATION
  • Hacettepe University Affiliated: Yes

Abstract

Modern technologies and recently developed digital solutions make their way into all aspects of lives of individuals and businesses, and manufacturing industry is no exception. In the era of digital revolution of industry, manufacturing processes can benefit from digitalization technologies immensely. Digital twin (DT) is a technology concept that aims to create a digital mirror of a physical system with a constant data flow between two components. This idea can be used for monitoring and optimization of the present system as well as forecasting and estimating future states of it. There have been theoretical and practical studies conducted on DT in manufacturing area. This systematic literature review (SLR) aims to summarize the current state of literature and shine a light on open areas for future research. Using a rigorous SLR method, 247 relevant studies from 2015 to 2020 are examined to answer a set of research questions. The current state of DT in manufacturing literature is analyzed and explained with an emphasis on where the future studies may go in this area.