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

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

doi:10.1002/adem.202402930

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

Ihsan Murat Kuşoğlu
, Sunidhi Garg
, Arvid Abel
, Prasanna V. Balachandran
, Stephan Barcikowski
, Louis Becker
, Jan Simeon Bernsmann
, Jonas Boseila
, Christoph Broeckmann
, Mert Coskun
, Malte Dreyer
, Mark East
, Mark Easton
, Nils Ellendt
, Stan Gann
, Bilal Gökce
, Mareen Goßling
, Joachim Greiner
, Piotr Gruber
, Moritz Grünewald

Kopila Gurung, Nick Hantke, Florian Hengsbach, Hannes Holländer, Brecht Van Hooreweder, Kay Peter Hoyer, Yajiang Huang, Florian Huber, Olaf Kessler, Burçin Özbay Kısasöz, Stefan Kleszczynski, Ebubekir Koc, Tomasz Kurzynowski, Arno Kwade, Simon Leupold, Dongmei Liu, Felix Lomo, Arne Lüddecke, Gerrit A. Luinstra, David A. Mauchline, Fabian Meyer, Lars Meyer, Peter Middendorf, Stefan Nolte, Michał Olejarczyk, Ludger Overmeyer, Andrij Pich, Sebastian Platt, Felix Radtke, Roland Ramm, Silja Katharina Rittinghaus, Richard Rothfelder, Johannes Rudloff, Mirko Schaper, Marie Luise Scheck, Johannes Henrich Schleifenbaum, Michael Schmidt, Jan T. Sehrt, Yvonne P. Shabanga, Alexander Sommereyns, Rabea Steuer, Layla Shams Tisha, Anastasiya Toenjes, Christopher Tuck, Adrian Vaghar, Bey Vrancken, Zhengze Wang, Sebastian Weber, Jan Wegner, Bai Xiang Xu, Yangyiwei Yang, Duyao Zhang, Evgeny Zhuravlev, Anna R. Ziefuss

University of Duisburg
University of Virginia
Laser Zentrum Hannover e.V.
Ruhr-Universität Bochum
RWTH Aachen University
Fatih University
University of Paderborn
University of Nottingham
RMIT University
Leibniz Institute for Materials Engineering - IWT
Universität Bremen
Bergische Universität Wuppertal
Universität Stuttgart
Wroclaw University of Science and Technology
SKZ - KFE gGmbH
Fraunhofer Institute for Manufacturing Engineering and Automation IPA
KU Leuven
Sichuan University
Universitaet Erlangen
Universität Rostock
Boğaziçi University
Technische Universitat Braunschweig
Friedrich-Schiller-Universität Jena
Universität Hamburg
Vaal University of Technology
Fraunhofer Institute for Applied Optics and Precision Engineering
Leibniz Universität Hannover
Technische Universität Darmstadt
Centre for Additive Manufacturing

Research output: Contribution to journal › Article › peer-review

5 Citations (SciVal)

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.

Original language	English
Article number	2402930
Journal	Advanced Engineering Materials
Volume	27
Issue number	14
Early online date	17 Jun 2025
DOIs	https://doi.org/10.1002/adem.202402930
Publication status	Published - 31 Jul 2025

Data Availability Statement

The data that support the findings will be available in [Duepublico] at [https://doi.org/10.17185/duepublico/82674] following an embargo from the date of publication to allow for commercialization of research findings.

Funding

The coordination office acknowledges financial support from the DFG under SPP2122 Project Number 359962234, \u201CNew Materials for Additive Manufacturing\u201D and Evonik Industries for donation of the PA12 powder. As part of the SPP 2122, several subprojects contributed to this study and gratefully acknowledged labor and research units. The respective internal DFG project numbers were as follows: 409784234, 493935114, 409779181, 493947509, 410107213, 409791748, 493860861, 409485213, 493889809, 409766389, 409621284, 409726740, and 493892776. Besides the DFG SPP2122, external institutions in Germany technically supported the study within DFG project numbers 432804018 and 503259970. The authors also acknowledge the support of external and international research facilities and the technical assistance of Paul Spithill from the RMIT Advanced Manufacturing Precinct for technical investigation; Tim Robert Brocksieper from Hybrid additive manufacturing, Ruhr University Bochum for data curation, Johann Sokurov from the Institute of Technical and Macromolecular Chemistry, University Hamburg, for measurement execution and data mediation; Johannes Wilke from Otto Schott Institute of Materials Research, Friedrich-Schiller from University Jena for technical visualization; Stephanie Lippmann from Institute of Applied Physics, Friedrich-Schiller from University Jena for resources, methodology, and validation; Adam Whitbread from Centre for Additive Manufacturing, University of Nottingham for technical support; Nils Wiethoff and Daniel Christopher Behrens from Chair of Materials Science and Additive Manufacturing, University of Wuppertal for investigation and validation; Marco Martin Barreiros from Chair of Materials Technology, Ruhr University Bochum for technical apprentice; Rabia Yesiltas and Tinahan Tez from ALUTEAM, Fatih Sultan Mehmet University for technical coordination and validation; Guangxian Li from College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering of China, Sichuan University for validation; \u00D6mer Ers\u00F6z from Technical Chemistry I, UDE for sampling metal and polymer feedstocks; and Sylvia Monsheimer, Evonik, for her advice in the industrial board of the SPP2122, as well as Felix M\u00FCller, Evonik, for steaditly supporting the networking activities Open Access funding enabled and organized by Projekt DEAL. The coordination office acknowledges financial support from the DFG under SPP2122 Project Number 359962234, \u201CNew Materials for Additive Manufacturing\u201D and Evonik Industries for donation of the PA12 powder. As part of the SPP 2122, several subprojects contributed to this study and gratefully acknowledged labor and research units. The respective internal DFG project numbers were as follows: 409784234, 493935114, 409779181, 493947509, 410107213, 409791748, 493860861, 409485213, 493889809, 409766389, 409621284, 409726740, and 493892776. Besides the DFG SPP2122, external institutions in Germany technically supported the study within DFG project numbers 432804018 and 503259970. The authors also acknowledge the support of external and international research facilities and the technical assistance of Paul Spithill from the RMIT Advanced Manufacturing Precinct for technical investigation; Tim Robert Brocksieper from Hybrid additive manufacturing, Ruhr University Bochum for data curation, Johann Sokurov from the Institute of Technical and Macromolecular Chemistry, University Hamburg, for measurement execution and data mediation; Johannes Wilke from Otto Schott Institute of Materials Research, Friedrich\u2010Schiller from University Jena for technical visualization; Stephanie Lippmann from Institute of Applied Physics, Friedrich\u2010Schiller from University Jena for resources, methodology, and validation; Adam Whitbread from Centre for Additive Manufacturing, University of Nottingham for technical support; Nils Wiethoff and Daniel Christopher Behrens from Chair of Materials Science and Additive Manufacturing, University of Wuppertal for investigation and validation; Marco Martin Barreiros from Chair of Materials Technology, Ruhr University Bochum for technical apprentice; Rabia Yesiltas and Tinahan Tez from ALUTEAM, Fatih Sultan Mehmet University for technical coordination and validation; Guangxian Li from College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering of China, Sichuan University for validation; \u00D6mer Ers\u00F6z from Technical Chemistry I, UDE for sampling metal and polymer feedstocks; and Sylvia Monsheimer, Evonik, for her advice in the industrial board of the SPP2122, as well as Felix M\u00FCller, Evonik, for steaditly supporting the networking activities

Funders	Funder number
ALUTEAM
Royal Melbourne Institute of Technology
Fatih Sultan Mehmet University
Sichuan University
State Key Laboratory of Polymer Materials Engineering
Evonik Industries	493892776, 409726740, 432804018, 493860861, 409621284, 410107213, 409485213, 503259970, 409779181, 493889809, 493947509, 493935114, 409766389, 409784234, 409791748
Deutsche Forschungsgemeinschaft (DFG)	SPP2122, 359962234

Keywords

AlSi10Mg
PA12
additive manufacturing
carbon black
laser powder bed fusion
machine learning
nanoparticles
repeatability
reproducibility
selective laser melting
selective laser sintering
siliconcarbide
silver
titaniumcarbide

ASJC Scopus subject areas

General Materials Science
Condensed Matter Physics

Access to Document

10.1002/adem.202402930Licence: CC BY

https://advanced.onlinelibrary.wiley.com/doi/10.1002/adem.202402930Licence: CC BY

Cite this

Kuşoğlu, I. M., Garg, S., Abel, A., Balachandran, P. V., Barcikowski, S., Becker, L., Bernsmann, J. S., Boseila, J., Broeckmann, C., Coskun, M., Dreyer, M., East, M., Easton, M., Ellendt, N., Gann, S., Gökce, B., Goßling, M., Greiner, J., Gruber, P., ... Ziefuss, A. R. (2025). Large-Scale Interlaboratory Study Along the Entire Process Chain of Laser Powder Bed Fusion: Bridging Variability, Standards, and Optimization across Metals and Polymers. Advanced Engineering Materials, 27(14), Article 2402930. https://doi.org/10.1002/adem.202402930

Large-Scale Interlaboratory Study Along the Entire Process Chain of Laser Powder Bed Fusion: Bridging Variability, Standards, and Optimization across Metals and Polymers. / Kuşoğlu, Ihsan Murat; Garg, Sunidhi; Abel, Arvid et al.
In: Advanced Engineering Materials, Vol. 27, No. 14, 2402930, 31.07.2025.

Research output: Contribution to journal › Article › peer-review

Kuşoğlu, IM, Garg, S, Abel, A, Balachandran, PV, Barcikowski, S, Becker, L, Bernsmann, JS, Boseila, J, Broeckmann, C, Coskun, M, Dreyer, M, East, M, Easton, M, Ellendt, N, Gann, S, Gökce, B, Goßling, M, Greiner, J, Gruber, P, Grünewald, M, Gurung, K, Hantke, N, Hengsbach, F, Holländer, H, Van Hooreweder, B, Hoyer, KP, Huang, Y, Huber, F, Kessler, O, Kısasöz, BÖ, Kleszczynski, S, Koc, E, Kurzynowski, T, Kwade, A, Leupold, S, Liu, D, Lomo, F, Lüddecke, A, Luinstra, GA, Mauchline, DA, Meyer, F, Meyer, L, Middendorf, P, Nolte, S, Olejarczyk, M, Overmeyer, L, Pich, A, Platt, S, Radtke, F, Ramm, R, Rittinghaus, SK, Rothfelder, R, Rudloff, J, Schaper, M, Scheck, ML, Schleifenbaum, JH, Schmidt, M, Sehrt, JT, Shabanga, YP, Sommereyns, A, Steuer, R, Tisha, LS, Toenjes, A, Tuck, C, Vaghar, A, Vrancken, B, Wang, Z, Weber, S, Wegner, J, Xu, BX, Yang, Y, Zhang, D, Zhuravlev, E & Ziefuss, AR 2025, 'Large-Scale Interlaboratory Study Along the Entire Process Chain of Laser Powder Bed Fusion: Bridging Variability, Standards, and Optimization across Metals and Polymers', Advanced Engineering Materials, vol. 27, no. 14, 2402930. https://doi.org/10.1002/adem.202402930

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

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.",

keywords = "AlSi10Mg, PA12, additive manufacturing, carbon black, laser powder bed fusion, machine learning, nanoparticles, repeatability, reproducibility, selective laser melting, selective laser sintering, siliconcarbide, silver, titaniumcarbide",

author = "Ku{\c s}oğlu, \{Ihsan Murat\} and Sunidhi Garg and Arvid Abel and Balachandran, \{Prasanna V.\} and Stephan Barcikowski and Louis Becker and Bernsmann, \{Jan Simeon\} and Jonas Boseila and Christoph Broeckmann and Mert Coskun and Malte Dreyer and Mark East and Mark Easton and Nils Ellendt and Stan Gann and Bilal G{\"o}kce and Mareen Go{\ss}ling and Joachim Greiner and Piotr Gruber and Moritz Gr{\"u}newald and Kopila Gurung and Nick Hantke and Florian Hengsbach and Hannes Holl{\"a}nder and \{Van Hooreweder\}, Brecht and Hoyer, \{Kay Peter\} and Yajiang Huang and Florian Huber and Olaf Kessler and Kısas{\"o}z, \{Bur{\c c}in {\"O}zbay\} and Stefan Kleszczynski and Ebubekir Koc and Tomasz Kurzynowski and Arno Kwade and Simon Leupold and Dongmei Liu and Felix Lomo and Arne L{\"u}ddecke and Luinstra, \{Gerrit A.\} and Mauchline, \{David A.\} and Fabian Meyer and Lars Meyer and Peter Middendorf and Stefan Nolte and Micha{\l} Olejarczyk and Ludger Overmeyer and Andrij Pich and Sebastian Platt and Felix Radtke and Roland Ramm and Rittinghaus, \{Silja Katharina\} and Richard Rothfelder and Johannes Rudloff and Mirko Schaper and Scheck, \{Marie Luise\} and Schleifenbaum, \{Johannes Henrich\} and Michael Schmidt and Sehrt, \{Jan T.\} and Shabanga, \{Yvonne P.\} and Alexander Sommereyns and Rabea Steuer and Tisha, \{Layla Shams\} and Anastasiya Toenjes and Christopher Tuck and Adrian Vaghar and Bey Vrancken and Zhengze Wang and Sebastian Weber and Jan Wegner and Xu, \{Bai Xiang\} and Yangyiwei Yang and Duyao Zhang and Evgeny Zhuravlev and Ziefuss, \{Anna R.\}",

year = "2025",

month = jul,

day = "31",

doi = "10.1002/adem.202402930",

language = "English",

volume = "27",

journal = "Advanced Engineering Materials",

issn = "1438-1656",

publisher = "Wiley-VCH Verlag",

number = "14",

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TY - JOUR

T1 - Large-Scale Interlaboratory Study Along the Entire Process Chain of Laser Powder Bed Fusion

T2 - Bridging Variability, Standards, and Optimization across Metals and Polymers

AU - Kuşoğlu, Ihsan Murat

AU - Garg, Sunidhi

AU - Abel, Arvid

AU - Balachandran, Prasanna V.

AU - Barcikowski, Stephan

AU - Becker, Louis

AU - Bernsmann, Jan Simeon

AU - Boseila, Jonas

AU - Broeckmann, Christoph

AU - Coskun, Mert

AU - Dreyer, Malte

AU - East, Mark

AU - Easton, Mark

AU - Ellendt, Nils

AU - Gann, Stan

AU - Gökce, Bilal

AU - Goßling, Mareen

AU - Greiner, Joachim

AU - Gruber, Piotr

AU - Grünewald, Moritz

AU - Gurung, Kopila

AU - Hantke, Nick

AU - Hengsbach, Florian

AU - Holländer, Hannes

AU - Van Hooreweder, Brecht

AU - Hoyer, Kay Peter

AU - Huang, Yajiang

AU - Huber, Florian

AU - Kessler, Olaf

AU - Kısasöz, Burçin Özbay

AU - Kleszczynski, Stefan

AU - Koc, Ebubekir

AU - Kurzynowski, Tomasz

AU - Kwade, Arno

AU - Leupold, Simon

AU - Liu, Dongmei

AU - Lomo, Felix

AU - Lüddecke, Arne

AU - Luinstra, Gerrit A.

AU - Mauchline, David A.

AU - Meyer, Fabian

AU - Meyer, Lars

AU - Middendorf, Peter

AU - Nolte, Stefan

AU - Olejarczyk, Michał

AU - Overmeyer, Ludger

AU - Pich, Andrij

AU - Platt, Sebastian

AU - Radtke, Felix

AU - Ramm, Roland

AU - Rittinghaus, Silja Katharina

AU - Rothfelder, Richard

AU - Rudloff, Johannes

AU - Schaper, Mirko

AU - Scheck, Marie Luise

AU - Schleifenbaum, Johannes Henrich

AU - Schmidt, Michael

AU - Sehrt, Jan T.

AU - Shabanga, Yvonne P.

AU - Sommereyns, Alexander

AU - Steuer, Rabea

AU - Tisha, Layla Shams

AU - Toenjes, Anastasiya

AU - Tuck, Christopher

AU - Vaghar, Adrian

AU - Vrancken, Bey

AU - Wang, Zhengze

AU - Weber, Sebastian

AU - Wegner, Jan

AU - Xu, Bai Xiang

AU - Yang, Yangyiwei

AU - Zhang, Duyao

AU - Zhuravlev, Evgeny

AU - Ziefuss, Anna R.

PY - 2025/7/31

Y1 - 2025/7/31

N2 - 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.

AB - 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.

KW - AlSi10Mg

KW - PA12

KW - additive manufacturing

KW - carbon black

KW - laser powder bed fusion

KW - machine learning

KW - nanoparticles

KW - repeatability

KW - reproducibility

KW - selective laser melting

KW - selective laser sintering

KW - siliconcarbide

KW - silver

KW - titaniumcarbide

UR - https://www.scopus.com/pages/publications/105008251022

U2 - 10.1002/adem.202402930

DO - 10.1002/adem.202402930

M3 - Article

AN - SCOPUS:105008251022

SN - 1438-1656

VL - 27

JO - Advanced Engineering Materials

JF - Advanced Engineering Materials

IS - 14

M1 - 2402930

ER -

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

Abstract

Data Availability Statement

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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