Data-driven Discovery of Manufacturing Processes and Performance from Worker Localisation

Ayse Aslan, Hanane El-Raoui, Jack Hanson, Gokula Vasantha, John Quigley, Jonathan Corney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationFlexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems
Subtitle of host publicationProceedings of FAIM 2023, June 18–22, 2023, Porto, Portugal, Volume 1: Modern Manufacturing
ISBN (Electronic)978-3-031-38241-3
ISBN (Print)978-3-031-38240-6
Publication statusE-pub ahead of print - 24 Aug 2023

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Keywords / Materials (for Non-textual outputs)

  • manufacturing process optimisation
  • industrial productivity
  • process mining
  • indoor positioning systems


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