The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Intelligent Manufacturing in the Context of Industry 4.0: A Review
Open accessAbstract
Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.
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Cites
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Cited by
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611
“AI centric technologies will be able to monitor and control processes in real time o ff ering signi fi cant e ffi ciencies over manual processes (Jain & Mosier, 1992; Zhong, Xu, Klotz, & Newman, 2017a).”
BackgroundIndustry 4.0 technologies: Implementation patterns in manufacturing companies
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.01.004 · Cited by 2,958
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952
A survey on digital twin: definitions, characteristics, applications, and design implications
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019) · IEEE Access · doi:10.1109/access.2019.2953499 · Cited by 1,480
Horváth, D., & Szabó, R. Z. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2019.05.021 · Cited by 1,281
Metadata
- Authors
- Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T.
- Venue
- Engineering
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 3
- Issue
- 5
- Pages
- 616-630
- ISSN
- 2095-8099 (ISSN-L)
- OpenAlex
- W2767346351
- Cited by
- 2,913
- References
- 7
- FWCI
- 235.72
- Type
- journal article
- Language
- en
- Open access
- gold
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- Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering. https://doi.org/10.1016/j.eng.2017.05.015
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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.
