Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61789
Title: Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0
Contributor(s): Shafiq, Syed Imran (author); Sanin, Cesar  (author)orcid ; Szczerbicki, Edward (author)
Publication Date: 2020-02
Early Online Version: 2020-01-21
DOI: 10.1080/01969722.2019.1705546
Handle Link: https://hdl.handle.net/1959.11/61789
Abstract: 

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.

Publication Type: Journal Article
Source of Publication: Cybernetics and Systems, 51(2), p. 84-102
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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