Title: An interoperable knowledge base for manufacturing resource and service capability

Authors: Yuanyuan Zhao; Quan Liu; Wenjun Xu; Xun W. Xu; Shiqiang Yu; Zude Zhou

Addresses: School of Information Engineering, Wuhan University of Technology, Wuhan, China ' School of Information Engineering, Wuhan University of Technology, Wuhan, China ' School of Information Engineering, Wuhan University of Technology, Wuhan, China ' Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand ' Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand ' School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China

Abstract: To realise collaborative utilisation of resource in cloud manufacturing, in this paper, an interoperable knowledge base of manufacturing resource and service capability (IKB-MRSC) is developed for service discovery and selection. First, a network-based structural knowledge model is constructed including items set, links set and rule set. The functional entities in STEP/STEP-NC are integrated with status information and expert experience to form the items set. Links set and rule set are defined. Based on this, an upper-level manufacturing service model is built including capability profile, process model and resource model. Then, OWL and SWRL are combined to offer the semantic and logic representation for knowledge model. OntoSTEP is adopted to transfer EXPRESS-based schema into ontology pattern. Rules are defined for reasoning service capability with its related resource capability. Underlying this, a triple-level service matchmaking strategy is proposed. Finally, implementation verifies the validity of the knowledge base. [Received April 28 2016; Revised August 1 2016; Accepted August 22 2016]

Keywords: knowledge base; manufacturing resource capability; manufacturing service; cloud manufacturing; ontology web language; OWL; semantic web rule language; SWRL.

DOI: 10.1504/IJMR.2017.083650

International Journal of Manufacturing Research, 2017 Vol.12 No.1, pp.20 - 43

Received: 28 Apr 2016
Accepted: 22 Aug 2016

Published online: 13 Apr 2017 *

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