Towards the 5th Industrial Revolution: A literature review and a framework for Process Optimization Based on Big Data Analytics and Semantics
1
Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras, 26504 Greece, Laboratory for Manufacturing Systems and Automation (LMS), Greece
Submission date: 2021-06-26
Final revision date: 2021-08-30
Acceptance date: 2021-08-31
Online publication date: 2021-09-03
Publication date: 2021-09-30
Corresponding author
Dimitris Mourtzis
Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras, 26504 Greece, Laboratory for Manufacturing Systems and Automation (LMS), University of Patras, Rio Patras, 26504, Patra/Achaia, Greece
Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras, 26504 Greece, Laboratory for Manufacturing Systems and Automation (LMS), University of Patras, Rio Patras, 26504, Patra/Achaia, Greece
Journal of Machine Engineering 2021;21(3):5-39
KEYWORDS
TOPICS
ABSTRACT
The digitalization of modern manufacturing systems has resulted to increasing data generation, also known as Big Data. Although there are several technologies and techniques under the term Data Analytics for gathering such data, their interpretation to information, and ultimately to knowledge remains in its infancy. Consequently, albeit engineers, currently can monitor the factory level, optimization is cut off of the data acquisition, and is based on data related methodologies. The focus should be pivoted on designing and developing suitable frameworks for integrating Big Data to process optimization based on the context of information gathered from the shopfloor. This paper aims is to investigate the opportunities and the gaps as well as the challenges arising in the current industrial landscape, towards the efficient utilization of Big Data, for process optimization based on the integration of semantics. To that end, a literature review is performed, and a data-based framework is presented.
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| eISSN: | 2391-8071 |
| ISSN: | 1895-7595 |
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