Mitigating Undesired Conditions in Flexible Production with Product–Process–Resource Asset Knowledge Graphs

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00389338" target="_blank" >RIV/68407700:21730/25:00389338 - isvavai.cz</a>

Result on the web

<a href="https://ceur-ws.org/Vol-4085/paper9.pdf" target="_blank" >https://ceur-ws.org/Vol-4085/paper9.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Mitigating Undesired Conditions in Flexible Production with Product–Process–Resource Asset Knowledge Graphs

Original language description

Contemporary industrial cyber-physical production systems (CPPS) composed of robotic workcells face significant challenges in the analysis of undesired conditions due to the flexibility of Industry 4.0 that disrupts traditional quality assurance mechanisms. This paper presents a novel industry-oriented semantic model called Product-Process-Resource Asset Knowledge Graph (PPR-AKG), which is designed to analyze and mitigate undesired conditions in flexible CPPS. Built on top of the well-proven Product-Process-Resource (PPR) model originating from ISA-95 and VDI-3682, a comprehensive OWL ontology addresses shortcomings of conventional model-driven engineering for CPPS, particularly inadequate undesired condition and error handling representation. The integration of semantic technologies with large language models (LLMs) provides intuitive interfaces for factory operators, production planners, and engineers to interact with the entire model using natural language. Evaluation with the use case addressing electric vehicle battery remanufacturing demonstrates that the PPR-AKG approach efficiently supports resource allocation based on explicitly represented capabilities as well as identification and mitigation of undesired conditions in production. The key contributions include (1) a holistic PPR-AKG model capturing multi-dimensional production knowledge, and (2) the useful combination of the PPR-AKG with LLM-based chatbots for human interaction.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of Industry, Doctoral Consortium, Posters and Demos of the 24th International Semantic Web Conference (ISWC-C 2025)

ISBN

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ISSN

1613-0073

e-ISSN

1613-0073

Number of pages

34

Pages from-to

2-35

Publisher name

CEUR Workshop Proceedings

Place of publication

Aachen

Event location

Nara

Event date

Nov 2, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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