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<p>Powder Bed Fusion with Laser Beam of Metals&#xa0;(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&#xa0;M&#xa0;300–4 multi-laser machine. An Extract, Transform, Load&#xa0;(ETL) pipeline autonomously captures machine data, including scan vectors as well as process signals, and organizes them into a Digital Shadow&#xa0;(DS). The DS is further extended by external data sources, such as Melt Pool Monitoring&#xa0;(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.</p>

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Quality assurance via a cyber physical system of a PBF-LB/M machine

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月3日

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  • Konstantin Poka,
  • Sozol Ali,
  • Waleed Saeed,
  • Benjamin Merz,
  • Martin Epperlein,
  • Kai Hilgenberg

摘要

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 for advancing PBF-LB/M in industrial applications, achieving greater consistency and scalability in production.