Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61852
Title: Manufacturing Data Analysis in Internet of Things/Internet of Data (IoT/IoD) Scenario
Contributor(s): Shafiq, Syed Imran (author); Szczerbicki, Edward (author); Sanin, Cesar  (author)orcid 
Publication Date: 2018
Early Online Version: 2018-02-01
DOI: 10.1080/01969722.2017.1418265
Handle Link: https://hdl.handle.net/1959.11/61852
Abstract: 

Computer integrated manufacturing (CIM) has enormous benefits as it increases the rate of production, reduces errors and production waste, and streamlines manufacturing sub-systems. However, there are some new challenges related to CIM operating in the Internet of Things/Internet of Data (IoT/IoD) scenarios associated with Industry 4.0 and cyber-physical systems. The main challenge is to deal with the massive volume of data flowing between various CIM components functioning in virtual settings of IoT. This paper proposes decisional DNA-based knowledge representation framework to manage the storage, analysis, and processing of data, information, and knowledge of a typical CIM. The framework utilizes the concept of virtual engineering object and virtual engineering process for developing knowledge models of various CIM components such as automatic storage and retrieval systems, automatic guided vehicles, robots, and numerically controlled machines. The proposed model is capable of capturing in real time the manufacturing data, information and knowledge at every stage of production, that is, at the object level, the process level, and at the factory level. The significance of this study is that it will support decision-making by reusing the experience, which will not only help in effective real-time data monitoring and processing, but also make CIM system intelligent and ready to function in the virtual Industry 4.0 environment.

Publication Type: Journal Article
Source of Publication: Cybernetics and Systems, 49(5-6), p. 280-295
Publisher: Taylor & Francis Inc
Place of Publication: United States of America
ISSN: 1087-6553
0196-9722
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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