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Quality assurance via a cyber physical system of a PBF-LB/M machine
- Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M) faces challenges in reproducibility and quality assurance, even for widely applied alloys like AlSi10Mg. This work introduces a digital provenance framework for PBF-LB/M, showcased through the EOS M 300–4 multi-laser machine. An Extract, Transform, Load (ETL) pipeline autonomously captures machine data, including scan vectors as well as process signals, and organizes them into a Digital Shadow (DS). The DS is further extended by external data sources, such as Melt Pool Monitoring (MPM), to enable comprehensive analysis and root cause identification. This approach ensures continuous data representation and facilitates the development of new quality metrics. Moreover, the framework enhances quality assurance and traceability, supports compliance with industry standards, and improves productivity. It also enables more precise cost calculations and predictive maintenance. By addressing these challenges, the framework is essential forPowder Bed Fusion with Laser Beam of Metals (PBF-LB/M) faces challenges in reproducibility and quality assurance, even for widely applied alloys like AlSi10Mg. This work introduces a digital provenance framework for PBF-LB/M, showcased through the EOS M 300–4 multi-laser machine. An Extract, Transform, Load (ETL) pipeline autonomously captures machine data, including scan vectors as well as process signals, and organizes them into a Digital Shadow (DS). The DS is further extended by external data sources, such as Melt Pool Monitoring (MPM), to enable comprehensive analysis and root cause identification. This approach ensures continuous data representation and facilitates the development of new quality metrics. Moreover, the framework enhances quality assurance and traceability, supports compliance with industry standards, and improves productivity. It also enables more precise cost calculations and predictive maintenance. By addressing these challenges, the framework is essential for advancing PBF-LB/M in industrial applications, achieving greater consistency and scalability in production.…


| Autor*innen: | Konstantin PokaORCiD, Sozol AliORCiD, Waleed SaeedORCiD, Benjamin MerzORCiD, Martin EpperleinORCiD, Kai HilgenbergORCiD |
|---|---|
| Dokumenttyp: | Zeitschriftenartikel |
| Veröffentlichungsform: | Verlagsliteratur |
| Sprache: | Englisch |
| Titel des übergeordneten Werkes (Englisch): | Progress in Additive Manufacturing |
| Jahr der Erstveröffentlichung: | 2025 |
| Organisationseinheit der BAM: | 9 Komponentensicherheit |
| 9 Komponentensicherheit / 9.6 Additive Fertigung metallischer Komponenten | |
| Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
| Verlag: | Springer Science and Business Media LLC |
| Jahrgang/Band: | 10 |
| Ausgabe/Heft: | 3 |
| Erste Seite: | 1771 |
| Letzte Seite: | 1783 |
| DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
| Freie Schlagwörter: | Data driven quality assurance; Data engineering; Digital shadow; PBF-LB/M |
| Themenfelder/Aktivitätsfelder der BAM: | Material |
| Material / Additive Fertigung | |
| DOI: | 10.1007/s40964-025-00978-w |
| URN: | urn:nbn:de:kobv:b43-625187 |
| ISSN: | 2363-9520 |
| Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
| Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
| Datum der Freischaltung: | 10.02.2025 |
| Referierte Publikation: | Ja |
| Datum der Eintragung als referierte Publikation: | 05.05.2025 |
| Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |


