The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Advances in Sensor Technologies in the Era of Smart Factory and Industry 4.0
Open accessAbstract
The evolution of intelligent manufacturing has had a profound and lasting effect on the future of global manufacturing. Industry 4.0 based smart factories merge physical and cyber technologies, making the involved technologies more intricate and accurate; improving the performance, quality, controllability, management, and transparency of manufacturing processes in the era of the internet-of-things (IoT). Advanced low-cost sensor technologies are essential for gathering data and utilizing it for effective performance by manufacturing companies and supply chains. Different types of low power/low cost sensors allow for greatly expanded data collection on different devices across the manufacturing processes. While a lot of research has been carried out with a focus on analyzing the performance, processes, and implementation of smart factories, most firms still lack in-depth insight into the difference between traditional and smart factory systems, as well as the wide set of different sensor technologies associated with Industry 4.0. This paper identifies the different available sensor technologies of Industry 4.0, and identifies the differences between traditional and smart factories. In addition, this paper reviews existing research that has been done on the smart factory; and therefore provides a broad overview of the extant literature on smart factories, summarizes the variations between traditional and smart factories, outlines different types of sensors used in a smart factory, and creates an agenda for future research that encompasses the vigorous evolution of Industry 4.0 based smart factories.
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Cites
Internet of Things for Smart Cities
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Industry 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
The expected contribution of Industry 4.0 technologies for industrial performance
Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018) · International Journal of Production Economics · doi:10.1016/j.ijpe.2018.08.019 · Cited by 2,201
Data-driven smart manufacturing
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.006 · Cited by 1,806
The future of manufacturing industry: a strategic roadmap toward Industry 4.0
Ghobakhloo, M. (2018) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-02-2018-0057 · Cited by 1,381
A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements
Manavalan, E., & Jayakrishna, K. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.11.030 · Cited by 1,054
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Micromachined pressure sensors: review and recent developments
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Industry 4.0 innovation ecosystems: An evolutionary perspective on value cocreation
Benitez, G. B., Ayala, N. F., & Frank, A. G. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2020.107735 · Cited by 508
Cited by
Interpretable polynomial neural ordinary differential equations
Fronk, C., & Petzold, L. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0130803 · Cited by 33
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Metadata
- Authors
- Kalsoom, T., Ramzan, N., Ahmed, S., & Ur-Rehman, M.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-11-27
- Volume
- 20
- Issue
- 23
- Pages
- 6783
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W3109763490
- Cited by
- 341
- References
- 49
- FWCI
- 25.09
- Type
- journal article
- Language
- en
- Open access
- gold
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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.