Knowledge Graph Supported Machine Parameterization for the Injection Moulding Industry

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

*Korrespondierende*r Autor*in für diese Arbeit

Publikation: Beitrag in Buch/KonferenzbandBeitrag in Konferenzband

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.

OriginalspracheEnglisch
Titel des SammelwerksKnowledge Graphs and Semantic Web
Untertitel des Sammelwerks4th Iberoamerican Conference and 3rd Indo-American Conference, KGSWC 2022, Proceedings
Herausgeber*innenBoris Villazón-Terrazas, Fernando Ortiz-Rodriguez, Sanju Tiwari, Miguel-Angel Sicilia, David Martín-Moncunill
ErscheinungsortCham
VerlagSpringer
Seiten106-120
Seitenumfang15
ISBN (elektronisch)9783031214226
ISBN (Print)9783031214219
DOIs
PublikationsstatusVeröffentlicht - 2022
Veranstaltung4th Iberoamerican and the 3rd Indo-American Knowledge Graphs and Semantic Web Conference, KGSWC 2022 - Madrid, Spanien
Dauer: 21 Nov. 202223 Nov. 2022

Publikationsreihe

ReiheCommunications in Computer and Information Science
Band1686 CCIS
ISSN1865-0929

Konferenz

Konferenz4th Iberoamerican and the 3rd Indo-American Knowledge Graphs and Semantic Web Conference, KGSWC 2022
Land/GebietSpanien
OrtMadrid
Zeitraum21/11/2223/11/22

Bibliographische Notiz

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

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