Knowledge Graph Supported Machine Parameterization for the Injection Moulding Industry

Stefan Bachhofner*, Kabul Kurniawan, Elmar Kiesling, Kate Revoredo, Dina Bayomie

*Corresponding author for this work

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

Abstract

Plastic injection moulding requires careful management of machine parameters to achieve consistently high product quality. To avoid quality issues and minimize productivity losses, initial setup as well as continuous adjustment of these parameters during production are critical. Stakeholders involved in the parameterization rely on experience, extensive documentation in guidelines and Failure Mode and Effects Analysis (FMEA) documents, as well as a wealth of sensor data to inform their decisions. This disparate, heterogeneous, and largely unstructured collection of information sources is difficult to manage across systems and stakeholders, and results in tedious processes. This limits the potential for knowledge transfer, reuse, and automated learning. To address this challenge, we introduce a knowledge graph that supports injection technicians in complex setup and adjustment tasks. We motivate and validate our approach with a machine parameter recommendation use case provided by a leading supplier in the automotive industry. To support this use case, we created ontologies for the representation of parameter adjustment protocols and FMEAs, and developed extraction components using these ontologies to populate the knowledge graph from documents. The artifacts created are part of a process-aware information system that will be deployed within a European project at multiple use case partners. Our ontologies are available at https://short.wu.ac.at/FMEA-AP, and the software at https://short.wu.ac.at/KGSWC2022.

Original languageEnglish
Title of host publicationKnowledge Graphs and Semantic Web
Subtitle of host publication4th Iberoamerican Conference and 3rd Indo-American Conference, KGSWC 2022, Proceedings
EditorsBoris Villazón-Terrazas, Fernando Ortiz-Rodriguez, Sanju Tiwari, Miguel-Angel Sicilia, David Martín-Moncunill
Place of PublicationCham
PublisherSpringer
Pages106-120
Number of pages15
ISBN (Electronic)9783031214226
ISBN (Print)9783031214219
DOIs
Publication statusPublished - 2022
Event4th Iberoamerican and the 3rd Indo-American Knowledge Graphs and Semantic Web Conference, KGSWC 2022 - Madrid, Spain
Duration: 21 Nov 202223 Nov 2022

Publication series

SeriesCommunications in Computer and Information Science
Volume1686 CCIS
ISSN1865-0929

Conference

Conference4th Iberoamerican and the 3rd Indo-American Knowledge Graphs and Semantic Web Conference, KGSWC 2022
Country/TerritorySpain
CityMadrid
Period21/11/2223/11/22

Bibliographical note

Funding Information:
Keywords: Semantic web · Knowledge graphs · Manufacturing process · Automotive industry · Failure mode and error analysis · Industry 4.0 This research has received funding from the Teaming.AI project, which is part of the European Union’s Horizon 2020 research and innovation program under grant agreement No. 957402.

Funding Information:
Acknowledgements. This work received funding from the Teaming.AI project in the European Union’s Horizon 2020 research and innovation program under grant agreement No. 95740.

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Automotive industry
  • Failure mode and error analysis
  • Industry 4.0
  • Knowledge graphs
  • Manufacturing process
  • Semantic web

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