Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61882
Title: Virtual engineering process (VEP): a knowledge representation approach for building bio-inspired distributed manufacturing DNA
Contributor(s): Shafiq, Syed Imran (author); Sanin, Cesar  (author)orcid ; Toro, Carlos (author); Szczerbicki, Edward (author)
Publication Date: 2016
Early Online Version: 2015-12-29
DOI: 10.1080/00207543.2015.1125545
Handle Link: https://hdl.handle.net/1959.11/61882
Abstract: 

The objective of this research is to provide a user-friendly and effective way of representing engineering processes for distributed manufacturing systems so that they can develop, accumulate and share knowledge. The basic definition and principle of the approach is introduced first and then the prototype version of the system is developed and demonstrated with case studies, which verify the feasibility of the proposed approach. This paper proposes a novel concept of virtual engineering process (VEP), which is experience-based knowledge representation of engineering processes. VEP is an extension of our previous work on virtual engineering object (VEO). VEP model includes complete process knowledge required to manufacture a component. This knowledge is captured from three distinctive aspects related to manufacturing. First, information about the manufacturing operations involved. Second, information about the resources/machines required to perform operations and third, information about process level decisions that are taken. It also aims to combine/share experience of engineering objects, manufacturing processes, and systems. It applies bio-inspired knowledge engineering approach called decisional DNA and set of experience-based knowledge representation.

Publication Type: Journal Article
Source of Publication: International Journal of Production Research, 54(23), p. 7129-7142
Publisher: Taylor & Francis
Place of Publication: United Kingdom
ISSN: 1366-588X
0020-7543
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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