Szczegóły publikacji
Opis bibliograficzny
Event-driven and scalable digital twin system for real-time non-destructive testing in industrial computational systems / Piotr HAJDER, Mateusz MOJŻESZKO, Filip HALLO, Lucyna HAJDER, Krzysztof REGULSKI, Krzysztof BANAŚ, Monika PERNACH, Danuta SZELIGA, Krzysztof BZOWSKI, Kazimierz MICHALIK, Adam MROZEK, Łukasz SZTANGRET, Marek WILKUS, Tomasz JAŻDŻEWSKI, Wojciech JĘDRYSIK, Rafał NADOLSKI, Andrzej OPALIŃSKI, Łukasz RAUCH // W: 2025 IEEE international conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) [Dokument elektroniczny] : 17-21 March 2025, Washington D. C., USA : proceedings / IEEE. — Wersja do Windows. — Dane tekstowe. — Piscataway, NJ : IEEE, cop. 2025. — (IEEE International Conference on Pervasive Computing and Communications Workshops ; ISSN 2836-5348). — e-ISBN: 979-8-3315-3553-7. — S. 152–157. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 157, Abstr.
Autorzy (18)
- AGHHajder Piotr
- AGHMojżeszko Mateusz
- AGHHallo Filip
- AGHHajder Lucyna
- AGHRegulski Krzysztof
- AGHBanaś Krzysztof
- AGHPernach Monika
- AGHSzeliga Danuta
- AGHBzowski Krzysztof
- AGHMichalik Kazimierz
- AGHMrozek Adam
- AGHSztangret Łukasz
- AGHWilkus Marek
- AGHJażdżewski Tomasz
- AGHJędrysik Wojciech
- AGHNadolski Rafał
- AGHOpaliński Andrzej
- AGHRauch Łukasz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 160876 |
|---|---|
| Data dodania do BaDAP | 2025-07-09 |
| Tekst źródłowy | URL |
| DOI | 10.1109/PerComWorkshops65533.2025.00057 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Conference on Pervasive Computing and Communications 2025 |
| Czasopismo/seria | IEEE International Conference on Pervasive Computing and Communications Workshops |
Abstract
This study presents a digital twin system designed to monitor, predict, and optimize linear welding parameters in real time, with applications in Industry 4.0 and smart manufacturing. The system leverages Kubernetes to seamlessly integrate various sensors, enabling their dynamic allocation to different aggregates and ensuring scalability for adding new devices as needed. The selected computational models, including machine learning and fuzzy logic-based approaches, provided effective real-time feedback to the welding aggregate by dynamically adjusting welding power. GitOps practices and event-driven communication using message queues facilitated efficient deployment and management in this dynamic and distributed environment, facilitating continuous system updates and minimizing downtime. By providing real-time feedback to welding machine operators and functioning as a digital twin, this system enhances both simulation capabilities and physical process control, demonstrating potential for cross-domain interoperability and advanced decision-making frameworks.
