Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook
By Radu F. Babiceanu, Remzi Seker. Computers in industry (Print), 2016
429 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/0ac065b6199e67d01c48f3f399014cb39bc3f8ef
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Hajar Fatorachian
- Alexandre Moeuf
- Rodolphe Barbaray
- Simon Tamayo-Giraldo
- Robert Pellerin
- Hadi Kazemi
- Samir Lamouri
- Robert X. Gao
In Industrial and Manufacturing Engineering
- Jinjiang Wang
- Hajar Fatorachian
- Alexandre Moeuf
- Robert Pellerin
- Hadi Kazemi
- Samir Lamouri
- Robert X. Gao
- Soundar Kumara *
Citations in the corpus
- The internet of things for smart manufacturing: A review (2019, background): “Babiceanu and Seker (2016) investigated trends in cyber-physical manufacturing systems.”
- Digital Twin for rotating machinery fault diagnosis in smart manufacturing (2018, background): “Recent advancement in the Internet of Things (IoTs), Cyber Physical System (CPS) and Big Data continuously reshapes modern manufacturing (Wang et al. 2015b; Babiceanu and Seker 2016; Zhong et al. 2017).”
- A critical investigation of Industry 4.0 in manufacturing: theoretical operationalisation framework (2018, background): “There between humans, machines and products (Babiceanu and Seker 2016; Einsiedler 2013).”
- Impact of Industry 4.0 on supply chain performance (2020, background): “…and virtual world through enabling high level of connectivity between software and hardware (Leitao, Colombo and Karnouskos 2016; Stojmenovic and Zhang 2015), and can lead to advanced communication between humans, machines, processes and products (Babiceanu and Seker 2016; Einsiedler 2013).”
- A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions (2019, background): “In the context of industrial big data, the problems of modeling and virtualization for CPS were discussed by (Babiceanu and Seker, 2016).”
- Big data analytics in supply chain management: A state-of-the-art literature review (2017, background): “…value, which represent the rigorousness of Big Data Analytics (BDA), are particularly important because without data analysis, other BD processing aspects such as collection, storage, and management would not create much value (Huang et al., 2015; Chen and Zhang, 2014; Babiceanu and Seker, 2016).”