The DOI matches its Crossref record; 158 of the 158 references it cites were found in a registry or an index.
Digital twins-based smart manufacturing system design in Industry 4.0: A review
Abstract
Abstract A smart manufacturing system (SMS) is a multi-field physical system with complex couplings among various components. Usually, designers in various fields can only design subsystems of an SMS based on the limited cognition of dynamics. Conducting SMS designs concurrently and developing a unified model to effectively imitate every interaction and behavior of manufacturing processes are challenging. As an emerging technology, digital twins can achieve semi-physical simulations to reduce the vast time and cost of physical commissioning/reconfiguration by the early detection of design errors/flaws of the SMS. However, the development of the digital twins concept in the SMS design remains vague. An innovative Function-Structure-Behavior-Control-Intelligence-Performance (FSBCIP) framework is proposed to review how digital twins technologies are integrated into and promote the SMS design based on a literature search in the Web of Science database. The definitions, frameworks, major design steps, new blueprint models, key enabling technologies, design cases, and research directions of digital twins-based SMS design are presented in this survey. It is expected that this survey will shed new light on urgent industrial concerns in developing new SMSs in the Industry 4.0 era.
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Cites
IOP Conference Series: Materials Science and Engineering
(2022) · IOP Conference Series: Materials Science and Engineering · doi:10.1088/1757-899x/1231/1/011001 · Cited by 10,357
Digital Twin in Industry: State-of-the-Art
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A Review of the Roles of Digital Twin in CPS-based Production Systems
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A survey on digital twin: definitions, characteristics, applications, and design implications
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Industry 5.0: Prospect and retrospect
Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis, D., & Wang, L. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.09.017 · Cited by 1,300
Self-citationTao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.06.015 · Cited by 1,255
Applications of Digital Twin across Industries: A Review
Singh, M., Srivastava, R., Fuenmayor, E., Kuts, V., Qiao, Y., Murray, N., & Devine, D. (2022) · Applied Sciences · doi:10.3390/app12115727 · Cited by 355
Ramu, S. P., Boopalan, P., Pham, Q.-V., Maddikunta, P. K. R., Huynh-The, T., Alazab, M., Nguyen, T. T., & Gadekallu, T. R. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103663 · Cited by 301
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Metadata
- Authors
- Leng, J., Wang, D., Shen, W., Li, X., Liu, Q., & Chen, X.
- Venue
- Journal of Manufacturing Systems
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 60
- Pages
- 119-137
- ISSN
- 0278-6125 (ISSN-L)
- OpenAlex
- W3164356889
- Cited by
- 766
- References
- 158
- FWCI
- 61.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- Leng, J., Wang, D., Shen, W., Li, X., Liu, Q., & Chen, X. (2021). Digital twins-based smart manufacturing system design in Industry 4.0: A review. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2021.05.011
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
