The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Requirements of the Smart Factory System: A Survey and Perspective
Open accessAbstract
With the development of Industry 4.0 and the emergence of the smart factory concept, the traditional philosophy of manufacturing systems will change. The smart factory introduces changes to the factors and elements of traditional manufacturing systems and incorporates the current requirements of smart systems so that it can compete in the future. An increasing amount of research in both academia and industry is dedicated to transitioning the concept of the smart factory from theory to practice. The purpose of the current research is to highlight the perspectives that shape the smart factory and to suggest approaches and technical support to enable the realization of those perspectives. This paper fills this gap by identifying and analyzing research on smart factories. We suggest a framework to analyze existing research and investigate the elements and features of smart factory systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Industry 4.0: A survey on technologies, applications and open research issues
Lu, Y. (2017) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2017.04.005 · Cited by 3,012
A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises
Schumacher, A., Erol, S., & Sihn, W. (2016) · Procedia CIRP · doi:10.1016/j.procir.2016.07.040 · Cited by 1,766 · cited 1×
“To compete in future markets, manufacturing companies should be able to produce small batch sizes of a product—or even a single item—in a timely and cost-effective manner, and they should have sufficient functionality, scalability, and connectivity with customers and suppliers to meet these requirements [2].”
BackgroundDigital twin shop-floor: a new shop-floor paradigm towards smart manufacturing
Tao, F., & Zhang, M. (2017) · IEEE Access · doi:10.1109/access.2017.2756069 · Cited by 1,397
Haag, S., & Anderl, R. (2018) · Manufacturing Letters · doi:10.1016/j.mfglet.2018.02.006 · Cited by 559
Kemény, Z., Beregi, R. J., Erdős, G., & Nacsa, J. (2016) · Procedia CIRP · doi:10.1016/j.procir.2016.05.060 · Cited by 52
A novel framework for virtual recommissioning in reconfigurable manufacturing systems
Mortensen, S. T., Chrysostomou, D., & Madsen, O. (2017) · 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa.2017.8247744 · Cited by 12
Cited by
Characterising the Digital Twin: A systematic literature review
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020) · CIRP Journal of Manufacturing Science and Technology · doi:10.1016/j.cirpj.2020.02.002 · Cited by 2,422
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
Lim, K. Y. H., Zheng, P., & Chen, C.-H. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01512-w · Cited by 700
Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature
O’Neill, T., McNeese, N., Barron, A., & Schelble, B. (2022) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820960865 · Cited by 695
Butt, J. (2020) · Designs · doi:10.3390/designs4030017 · Cited by 247
Communication Protocols of an Industrial Internet of Things Environment: A Comparative Study
Jaloudi, S. (2019) · Future Internet · doi:10.3390/fi11030066 · Cited by 131
SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic
Rahman, A., Chakraborty, C., Anwar, A., Karim, M. R., Islam, M. J., Kundu, D., Rahman, Z., & Band, S. S. (2022) · Cluster Computing · doi:10.1007/s10586-021-03367-4 · Cited by 121
Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
Kasper, L., Birkelbach, F., Schwarzmayr, P., Steindl, G., Ramsauer, D., & Hofmann, R. (2022) · Applied Sciences · doi:10.3390/app12146981 · Cited by 33
Dymora, P., & Paszkiewicz, A. (2020) · Applied Sciences · doi:10.3390/app10238521 · Cited by 18
A Modular Smart Vision System for Industrial Inspection and Control of Conformity
El Helou, M., Benfriha, K., Al-Ahmari, A. M., Wardle, P., Talhi, E., Loubère, S., El Zant, C., & Charrier, Q. (2022) · Smart and Sustainable Manufacturing Systems · doi:10.1520/ssms20220003 · Cited by 7
Self-citation
Metadata
- Authors
- Mabkhot, M. M., Al-Ahmari, A. M., Salah, B., & Alkhalefah, H.
- Venue
- Machines
- Publisher
- MDPI AG
- Published
- 2018-06-01
- Volume
- 6
- Issue
- 2
- Pages
- 23
- ISSN
- 2075-1702 (ISSN-L)
- OpenAlex
- W2807555578
- Cited by
- 282
- References
- 6
- FWCI
- 23.05
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.
