A knowledge graph approach for state-of-the-art implementation of industrial factory movement tracking system
Vasantha, Gokula and Aslan, Ayse and Hanson, Jack and El-Raoui, Hanane and Corney, Jonathan and Quigley, John; (2023) A knowledge graph approach for state-of-the-art implementation of industrial factory movement tracking system. In: The 32nd Flexible Automation and Intelligent Manufacturing International Conference (FAIM2023) : Lecture Notes in Mechanical Engineering (LNME). Springer, [Cham].
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Abstract
Digital sensing technologies are essential for realizing Industry 4.0, as they enhance productivity, assist with real-time decision-making, and provide flexibility and agility in manufacturing factories. However, implementing these technologies can be a significant challenge due to the need to consider various factors in manufacturing factories, such as heterogeneous equipment, fragmented knowledge, customization requirements, multiple alternative technologies, and the substantial costs involved in the trial-and-error process. A Knowledge Graph (KG) approach is proposed to streamline the implementation of the factory movement tracking system. The KG approach utilizes a knowledge representation reference model that integrates manufacturing objective, activity, resource, environment, factory movement, data, infrastructure, and decision support system. This reference model aids in classifying key phrases extracted from research abstracts and establishing knowledge relationships among them. A synthesized KG, created by analyzing thirty research abstracts, has correctly answered search queries about implementing the factory movement tracking system. This approach establishes a pathway for developing a software system to support movement tracking implementation through automatic interpretation, reasoning, and suggestions.
ORCID iDs
Vasantha, Gokula, Aslan, Ayse, Hanson, Jack, El-Raoui, Hanane ORCID: https://orcid.org/0000-0002-9079-3248, Corney, Jonathan ORCID: https://orcid.org/0000-0003-1210-3827 and Quigley, John ORCID: https://orcid.org/0000-0002-7253-8470;-
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Item type: Book Section ID code: 85119 Dates: DateEvent18 June 2023Published18 June 2023Published Online21 February 2023AcceptedSubjects: Social Sciences > Industries. Land use. Labor > Management. Industrial Management
Science > Mathematics > Electronic computers. Computer scienceDepartment: Strathclyde Business School > Management Science Depositing user: Pure Administrator Date deposited: 13 Apr 2023 12:30 Last modified: 11 Nov 2024 15:32 URI: https://strathprints.strath.ac.uk/id/eprint/85119