International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
line decor
  
line decor

 Volume 8, Issue 8 (August 2021), Pages: 20-30

----------------------------------------------

 Original Research Paper

 Title: Context-aware architecture for Industry 4.0-ready manufacturing facility

 Author(s): Olayan Alharbi 1, *, Mafawez Alharbi 2

 Affiliation(s):

 1Department of Computer Science, College of Science and Humanities at Rumah, Majmaah University, AlMajmaah, Saudi Arabia
 2Department of Natural and Applied Science, Community College Buraydah, Qassim University, Buraydah, Saudi Arabia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-7503-9960

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2021.08.003

 Abstract:

The industry 4.0 revolution is empowering the manufacturing sector with several advantages from the production to consumption stage of products, or beyond that. Recently, operators in factories have been accumulating extensive data from machine sensors and other organizational and operational technologies such as company enterprise and planning systems. Notably, having access to extensive data is a double-edged sword. To the best of our knowledge, there is not any work in the literature that proposed architecture for industry 4.0 based on a context-aware system. The aim of this research is to provide the context-aware architecture to enhance decision-making in factories and reduce the exposure of operators to the necessary and related findings. The proposed system is contextually aware of three aspects, operator feedback for previous similar findings, specifications of products under production, and historical data of manufacturing machines. The proposed system is proactive which attracted operator attention only when the findings were contextually related, based on the aforementioned aspects. The contributions of this research an intelligent architecture, a case study, and a mathematical model. 

 © 2021 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Cloud architectures, Patterns and tactics, Pattern catalog

 Article History: Received 2 February 2021, Received in revised form 16 April 2021, Accepted 27 April 2021

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Alharbi O and Alharbi M (2021). Context-aware architecture for Industry 4.0-ready manufacturing facility. International Journal of Advanced and Applied Sciences, 8(8): 20-30

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6

 Tables

 Table 1 Table 2 

----------------------------------------------

 References (18)

  1. Abowd GD, Dey AK, Brown PJ, Davies N, Smith M, and Steggles P (1999). Towards a better understanding of context and context-awareness. In the International Symposium on Handheld and Ubiquitous Computing, Springer, Karlsruhe, Germany: 304-307. https://doi.org/10.1007/3-540-48157-5_29   [Google Scholar]
  2. Agarwal R and Weill P (2012). The benefits of combining data with empathy. MIT Sloan Management Review, 54(1): 35-41.   [Google Scholar]
  3. Alharbi M (2014). Context-aware personal learning environment. Ph.D. Dissertation, De Montfort University, Leicester, UK.   [Google Scholar]
  4. Alharbi MT, Platt A, and Al-Bayatti AH (2012). Context-aware personal learning environment. In the International Conference for Internet Technology and Secured Transactions, IEEE, London, UK: 692-697.   [Google Scholar]
  5. Baldauf M, Dustdar S, and Rosenberg F (2007). A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing, 2(4): 263-277. https://doi.org/10.1504/IJAHUC.2007.014070   [Google Scholar]
  6. Colombo AW and Karnouskos S (2009). Towards the factory of the future: A service-oriented cross-layer infrastructure. In: European Telecommunications Standards Institute (Ed.), ICT shaping the world: A scientific view: 65-81. Wiley, Hoboken, USA.   [Google Scholar]
  7. Dey AK (2000). Providing architectural support for building context-aware applications. Ph.D. Dissertation, Georgia Institute of Technology, Atlanta, USA.   [Google Scholar]
  8. Gao R, Wang L, Teti R, Dornfeld D, Kumara S, Mori M, and Helu M (2015). Cloud-enabled prognosis for manufacturing. CIRP Annals, 64(2): 749-772. https://doi.org/10.1016/j.cirp.2015.05.011   [Google Scholar]
  9. Jardine AK, Lin D, and Banjevic D (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7): 1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012   [Google Scholar]
  10. Lee J, Kao HA, and Yang S (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP, 16: 3-8. https://doi.org/10.1016/j.procir.2014.02.001   [Google Scholar]
  11. Lee J, Lapira E, Bagheri B, and Kao HA (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1): 38-41. https://doi.org/10.1016/j.mfglet.2013.09.005   [Google Scholar]
  12. Lee MX, Lee YC, and Chou CJ (2017). Essential implications of the digital transformation in industry 4.0. Journal of Scientific and Industrial Research, 76: 465-467.   [Google Scholar]
  13. Peruzzini M, Grandi F, and Pellicciari M (2017). Benchmarking of tools for user experience analysis in Industry 4.0. Procedia Manufacturing, 11: 806-813. https://doi.org/10.1016/j.promfg.2017.07.182   [Google Scholar]
  14. Ryan NS, Pascoe J, and Morse DR (1998). Enhanced reality fieldwork: The context-aware archaeological assistant. In: Gaffney V, van Leusen M, and Exxon S (Eds.), Computer applications in archaeology: 182-196. Tempus Reparatum Publisher, Oxford, UK.   [Google Scholar]
  15. Teti R, Jemielniak K, O’Donnell G, and Dornfeld D (2010). Advanced monitoring of machining operations. CIRP Annals, 59(2): 717-739. https://doi.org/10.1016/j.cirp.2010.05.010   [Google Scholar]
  16. Uhlemann THJ, Lehmann C, and Steinhilper R (2017). The digital twin: Realizing the cyber-physical production system for industry 4.0. Procedia CIRP, 61: 335-340. https://doi.org/10.1016/j.procir.2016.11.152   [Google Scholar]
  17. Want R, Hopper A, Falcao V, and Gibbons J (1992). The active badge location system. ACM Transactions on Information Systems (TOIS), 10(1): 91-102. https://doi.org/10.1145/128756.128759   [Google Scholar]
  18. Zhong RY, Xu X, Klotz E, and Newman ST (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3(5): 616-630. https://doi.org/10.1016/J.ENG.2017.05.015   [Google Scholar]