Browsing by Author "Coskun, Mert"

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    Energy density optimization in laser-based powder bed fusion of nano-modified PA12 powder feedstocks
    (2025) Grünewald, Moritz; Ziefuss, Anna R.; Schlör, Christian; Popp, Kevin; Gann, Stan; Kusoglu, Ihsan Murat; Barcikowski, Stephan; Greiner, Joachim; Middendorf, Peter; Gruber, Piotr; Olejarczyk, Michał; Kurzynowski, Tomasz; Wang, Zhengze; Huang, Yajiang; Coskun, Mert; Kısasöz, Burçin Özbay; Koc, Ebubekir; Rudloff, Johannes
    Additive manufacturing (AM) by powder bed fusion using a laser beam (PBF-LB) is often considered as the process of choice for industrial applications. Small changes in process parameters and intrinsic or extrinsic material parameters can significantly affect the final as-built part properties in this highly complex AM process. Choosing a machine configuration with optimal process parameters can be time-consuming, especially when new powders, i.e., nano-modified feedstocks or batches, are used. Even if a set of parameters works for one PBF-LB machine, the same parameters may produce unsatisfactory results on another machine model. Dimensionless parameters can be beneficial in simplifying complex phenomena. In this study, a semi-analytical approach based on printing monolayers was semi-blind tested by five laboratories on different PBF-LB machines. Virgin polyamide 12 (PA12), silver, and carbon black nano-particle-modified PA12 powders were tested. Two dimensionless numbers were used to describe the process. The first number describes the energy conversion dimensionless. The second dimensionless number proposes a minimum energy input demand for full densification. Both numbers combine process parameters (e.g., laser power, scan speed) and material properties (latent heat and solid density), while energy conversion number is considered by experimental results (monolayer thickness). Results indicate how nanoparticles influence thermal conductivity and energy absorption. Suggested surface energy densities based on the dimensionless numbers were compared with mechanical properties. The prediction of the highest overall mechanical values (tensile strength and elongation at break) matched the best mechanical properties. The monolayer approach presents an experimental simple method for predicting suitable machine settings and narrowing the process window in an efficient and material-conserving way.
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    Large‐scale interlaboratory study along the entire process chain of laser powder bed fusion : bridging variability, standards, and optimization across metals and polymers
    (2025) Kuşoğlu, Ihsan Murat; Garg, Sunidhi; Abel, Arvid; Balachandran, Prasanna V.; Barcikowski, Stephan; Becker, Louis; Bernsmann, Jan-Simeon; Boseila, Jonas; Broeckmann, Christoph; Coskun, Mert; Dreyer, Malte; East, Mark; Easton, Mark; Ellendt, Nils; Gann, Stan; Gökce, Bilal; Goßling, Mareen; Greiner, Joachim; Gruber, Piotr; Grünewald, Moritz; Gurung, Kopila; Hantke, Nick; Hengsbach, Florian; Holländer, Hannes; Van Hooreweder, Brecht; Hoyer, Kay-Peter; Huang, Yajiang; Huber, Florian; Kessler, Olaf; Kısasöz, Burçin Özbay; Kleszczynski, Stefan; Koc, Ebubekir; Kurzynowski, Tomasz; Kwade, Arno; Leupold, Simon; Liu, Dongmei; Lomo, Felix; Lüddecke, Arne; Luinstra, Gerrit A.; Mauchline, David A.; Meyer, Fabian; Meyer, Lars; Middendorf, Peter; Nolte, Stefan; Olejarczyk, Michał; Overmeyer, Ludger; Pich, Andrij; Platt, Sebastian; Radtke, Felix; Ramm, Roland; Rittinghaus, Silja-Katharina; Rothfelder, Richard; Rudloff, Johannes; Schaper, Mirko; Scheck, Marie Luise; Schleifenbaum, Johannes Henrich; Schmidt, Michael; Sehrt, Jan T.; Shabanga, Yvonne P.; Sommereyns, Alexander; Steuer, Rabea; Tisha, Layla Shams; Toenjes, Anastasiya; Tuck, Christopher; Vaghar, Adrian; Vrancken, Bey; Wang, Zhengze; Weber, Sebastian; Wegner, Jan; Xu, Bai-Xiang; Yang, Yangyiwei; Zhang, Duyao; Zhuravlev, Evgeny; Ziefuss, Anna R.
    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.