Large‐Scale Interlaboratory Study Along the Entire Process Chain of Laser Powder Bed Fusion: Bridging Variability, Standards, and Optimization across Metals and Polymers

Laser powder bed fusion is a cornerstone technology for additive manufacturing (AM) of metals and polymers, yet challenges in achieving consistent reproducibility and process optimization persist. Addressing these requires a systematic understanding of the interactions between feedstock, process parameters, and final part characteristics throughout the entire production chain. This study presents results from a comprehensive interlaboratory investigation conducted by 32 research institutions, evaluating six feedstock, including nanoparticle‐modified aluminum alloy and polyamide powders, under standardized protocols. Data analysis encompasses 69 powder properties, 15 process parameters per print, and 78 part features, culminating in a dataset of over 1.2 million correlations. Advanced statistical methods and machine learning are employed to identify critical variability drivers, such as the impact of nanoparticle modifications on powder flowability and thermal conductivity, as well as the influence of process parameters on reproducibility. Newly introduced dimensionless figures of merit provide universal metrics to describe and predict thermal and mechanical interactions, simplifying process optimization and material characterization. The findings, supported by an open‐access dataset adhering to findable, accessible, interoperable, and reusable principles, advance understanding of material–process–structure–property relationships. They establish a benchmark for future research and lay the foundation for improving the reliability, quality, and sustainability of AM processes.

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Publication Details

Journal
Advanced Engineering Materials
Published
2025-06-17
DOI
https://doi.org/10.1002/adem.202402930
Citations
4
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
2.85

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article

Large‐Scale Interlaboratory Study Along the Entire Process Chain of Laser Powder Bed Fusion: Bridging Variability, Standards, and Optimization across Metals and Polymers

Nils Ellendt, Bey Vrancken, Johannes Rudloff, Jan Wegner et al.
4 citations
Advanced Engineering Materials
Additive Manufacturing Materials and Processes
2.85
article

Large‐Scale Interlaboratory Study Along the Entire Process Chain of Laser Powder Bed Fusion: Bridging Variability, Standards, and Optimization across Metals and Polymers

Nils Ellendt, Bey Vrancken, Johannes Rudloff, Jan Wegner, Arno Kwade, Florian Hengsbach, İhsan Murat Kuşoğlu, Sebastian Platt, Andrij Pich, Louis Becker, Stan Gann, Piotr Gruber, Anastasiya Toenjes, Christopher Tuck, Evgeny Zhuravlev, Gerrit A. Luinstra, Stephan Barcikowski, Johannes Henrich Schleifenbaum, Kopila Gurung, Yangyiwei Yang, Arne Lüddecke, Malte Dreyer, Christoph Broeckmann, Kay‐Peter Hoyer, Mark Easton, Mareen Goßling, Brecht Van Hooreweder, Mark East, Michael Schmidt, Sebastian Weber, Florian Huber, Peter Middendorf, Anna Rosa Ziefuß, Duyao Zhang, Roland Ramm, Ludger Overmeyer, Michał Olejarczyk, Marie Luise Scheck, R. Steuer, Silja‐Katharina Rittinghaus, Sunidhi Garg, Nick Hantke, Bilal Gökce, Bai‐Xiang Xu, Ebubekir Koç, Jan T. Sehrt, Olaf Keßler, Felix Radtke, David A. Mauchline, Yajiang Huang, Joachim Greiner, Mirko Schaper, Stefan Nolte, Moritz Grünewald, Simon Leupold, Stefan Kleszczynski, Richard Rothfelder, Alexander Sommereyns, Burçin Özbay Kısasöz, Zhengze Wang, F.N. Lomo, Jan-Simeon Ludger Bernsmann, Jonas Boseila, Adrian Vaghar, Hannes Holländer, Mert Coşkun, Dongmei Liu, Prasanna V. Balachandran, Fabian Meyer, Arvid Abel, Lars Meyer, Tomasz Kurzynowski, Layla Shams Tisha, Yvonne P. Shabanga
article en
4 citations

Abstract

Laser powder bed fusion is a cornerstone technology for additive manufacturing (AM) of metals and polymers, yet challenges in achieving consistent reproducibility and process optimization persist. Addressing these requires a systematic understanding of the interactions between feedstock, process parameters, and final part characteristics throughout the entire production chain. This study presents results from a comprehensive interlaboratory investigation conducted by 32 research institutions, evaluating six feedstock, including nanoparticle‐modified aluminum alloy and polyamide powders, under standardized protocols. Data analysis encompasses 69 powder properties, 15 process parameters per print, and 78 part features, culminating in a dataset of over 1.2 million correlations. Advanced statistical methods and machine learning are employed to identify critical variability drivers, such as the impact of nanoparticle modifications on powder flowability and thermal conductivity, as well as the influence of process parameters on reproducibility. Newly introduced dimensionless figures of merit provide universal metrics to describe and predict thermal and mechanical interactions, simplifying process optimization and material characterization. The findings, supported by an open‐access dataset adhering to findable, accessible, interoperable, and reusable principles, advance understanding of material–process–structure–property relationships. They establish a benchmark for future research and lay the foundation for improving the reliability, quality, and sustainability of AM processes.

Advanced Engineering MaterialsVol. 27(14)
University of Stuttgart (DE), Leibniz University Hannover (DE), Wrocław University of Science and Technology (PL), Vaal University of Technology (ZA), University of Nottingham (GB), Universität Hamburg (DE), University of Wuppertal (DE), University of Bremen (DE), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Paderborn University (DE), Sichuan University (CN), Fraunhofer Institute for Manufacturing Engineering and Automation (DE), Technische Universität Darmstadt (DE), Laser Zentrum Hannover (DE), Leibniz-Institut für Werkstofforientierte Technologien - IWT (DE), Fraunhofer Institute for Applied Optics and Precision Engineering (DE), Flanders Make (Belgium) (BE), Fatih Sultan Mehmet Waqf University (TR), Boğaziçi University (TR), University of Rostock (DE), University of Virginia (US), University of Bath (GB), University of Duisburg-Essen (DE), AGH University of Krakow (PL), Friedrich Schiller University Jena (DE), RMIT University (AU), RWTH Aachen University (DE), Ruhr University Bochum (DE), Technische Universität Braunschweig (DE), KU Leuven (BE)
Bergische Universität Wuppertal, Deutsche Forschungsgemeinschaft, RMIT University, Sichuan University, State Key Laboratory of Polymer Materials Engineering, Evonik Industries, Division of Materials Research
Responsible consumption and production
Openalex Percentile: Top 9%
Additive Manufacturing Materials and Processes
2.85
DOIOpen Access PDFOpenAlex
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