Knowledge-graph based approach for automated selection of spare parts suitable for additive manufacturing: a railway use-case

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dc.contributor.authorMadreiter, Theresa
dc.contributor.authorBesinger, Philipp
dc.contributor.authorArchila, Sebastian
dc.contributor.authorKohl, Linus
dc.contributor.authorAnsari, Fazel
dc.date.accessioned2024-09-06T14:13:55Z
dc.date.available2024-09-06T14:13:55Z
dc.date.freetoread2024-09-06
dc.date.issued2024-06-07
dc.date.pubOnline2024-09-06
dc.description.abstractSpare part inventory management (SPIM) in the railway sector highly demands reliability and transparency for decentralized inventory control. Optimal SPIM should ensure the availability of needed spare parts for a service request, considering the frequency of use and criticality criterion for effective maintenance. Additive manufacturing (AM) technologies enable costeffective production of small batch sizes often required for spare parts. However, critical component-specific information is often unstructured within engineering drawings (ED), making digital processing, and linking to existing data from enterprise resource planning (ERP) and maintenance management systems difficult. To ensure effective maintenance logistics, this paper introduces a knowledge graph (KG) that can facilitate i) interlinking multiple sources through data integration and ii) establishing a semantic data hub, thus serving as a backbone for automated assessment of component's suitability for AM. The proposed KG-based approach merges relevant (existing) ontologies, multi-structured data from ED, ERP system information, and external data sources. The approach is developed and evaluated in real-world use-cases in cooperation with the Austrian railway and public transit industry.
dc.description.conferencename12th International Conference on Through-life Engineering Services – TESConf2024
dc.description.sponsorshipThe authors would like to thank and acknowledge Austrian Research Funding Agency (FFG) for supporting this research through AM4Rail project.
dc.identifier.citationMadreiter T, Besinger P, Archila S, et al., (2024) Knowledge-graph based approach for automated selection of spare parts suitable for additive manufacturing: a railway use-case. 12th International Conference on Through-life Engineering Services – TESConf2024, 6-7 June 2024, Cranfield, UK
dc.identifier.doi10.57996/cran.ceres-2625
dc.identifier.urihttps://doi.org/10.57996/cran.ceres-2625
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22905
dc.language.isoen
dc.publisherCranfield University
dc.publisher.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22905
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSpare part inventory management
dc.subjectknowledge graph
dc.subjectadditive manufacturing
dc.subjectrailway industry
dc.titleKnowledge-graph based approach for automated selection of spare parts suitable for additive manufacturing: a railway use-case
dc.typeConference paper
dcterms.dateAccepted2024-03-25
dcterms.temporal.endDate07-Jun-2024
dcterms.temporal.startDate06-Jun-2024

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12th International Conference on Through-life Engineering Services 2024