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Type: Text × Publisher: Amsterdam [u.a.] : Elsevier Science × Subject: Additive manufacturing × WGL Institute: IFWD ×

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Now showing 1 - 5 of 5
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    Experimental and numerical characterization of imperfect additively manufactured lattices based on triply periodic minimal surfaces
    (Amsterdam [u.a.] : Elsevier Science, 2023) Günther, Fabian; Pilz, Stefan; Hirsch, Franz; Wagner, Markus; Kästner, Markus; Gebert, Annett; Zimmermann, Martina
    Lattices based on triply periodic minimal surfaces (TPMS) are attracting increasing interest in seminal industries such as bone tissue engineering due to their excellent structure-property relationships. However, the potential can only be exploited if their structural integrity is ensured. This requires a fundamental understanding of the impact of imperfections that arise during additive manufacturing. Therefore, in the present study, the structure-property relationships of eight TPMS lattices, including their imperfections, are investigated experimentally and numerically. In particular, the focus is on biomimetic network TPMS lattices of the type Schoen I-WP and Gyroid, which are fabricated by laser powder bed fusion from the biocompatible alloy Ti-42Nb. The experimental studies include computed tomography measurements and compression tests. The results highlight the importance of process-related imperfections on the mechanical performance of TPMS lattices. In the numerical work, firstly the as-built morphology is artificially reconstructed before finite element analyses are performed. Here, the reconstruction procedure previously developed by the same authors is used and validated on a larger experimental matrix before more advanced calculations are conducted. Specifically, the reconstruction reduces the numerical overestimation of stiffness from up to 341% to a maximum of 26% and that of yield strength from 66% to 12%. Given a high simulation accuracy and flexibility, the presented procedure can become a key factor in the future design process of TPMS lattices.
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    Machine learning for additive manufacturing: Predicting materials characteristics and their uncertainty
    (Amsterdam [u.a.] : Elsevier Science, 2023) Chernyavsky, Dmitry; Kononenko, Denys Y.; Han, Jun Hee; Kim, Hwi Jun; van den Brink, Jeroen; Kosiba, Konrad
    Additive manufacturing (AM) is known for versatile fabrication of complex parts, while also allowing the synthesis of materials with desired microstructures and resulting properties. These benefits come at a cost: process control to manufacture parts within given specifications is very challenging due to the relevance of a large number of processing parameters. Efficient predictive machine learning (ML) models trained on small datasets, can minimize this cost. They also allow to assess the quality of the dataset inclusive of uncertainty. This is important in order for additively manufactured parts to meet property specifications not only on average, but also within a given variance or uncertainty. Here, we demonstrate this strategy by developing a heteroscedastic Gaussian process (HGP) model, from a dataset based on laser powder bed fusion of a glass-forming alloy at varying processing parameters. Using amorphicity as the microstructural descriptor, we train the model on our Zr52.5Cu17.9Ni14.6Al10Ti5 (at.%) alloy dataset. The HGP model not only accurately predicts the mean value of amorphicity, but also provides the respective uncertainty. The quantification of the aleatoric and epistemic uncertainty contributions allows to assess intrinsic inaccuracies of the dataset, as well as identify underlying physical phenomena. This HGP model approach enables to systematically improve ML-driven AM processes.
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    Laser powder bed fusion of a superelastic Cu-Al-Mn shape memory alloy
    (Amsterdam [u.a.] : Elsevier Science, 2021) Babacan, N.; Pauly, S.; Gustmann, T.
    Dense and crack-free specimens of the shape memory alloy Cu71.6Al17Mn11.4 (at.%) were produced via laser powder bed fusion across a wide range of process parameters. The microstructure, viz. grain size, can be directly tailored within the process and with it the transformation temperatures (TTs) shifted to higher values by raising the energy input. The microstructure, and the superelastic behavior of additively manufactured samples were assessed by a detailed comparison with induction melted material. The precipitation of the α phase, which inhibit the martensitic transformation, were not observed in the additively manufactured samples owing to the high intrinsic cooling rates during the fabrication process. Fine columnar grains with a strong [001]-texture along the building direction lead to an enhanced yield strength compared to the coarse-grained cast samples. A maximum recoverable strain of 2.86% was observed after 5% compressive loading. The first results of our approach imply that laser powder bed fusion is a promising technique to directly produce individually designed Cu-Al-Mn shape memory parts with a pronounced superelasticity at room temperature.
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    Additively manufactured AlSi10Mg lattices – Potential and limits of modelling as-designed structures
    (Amsterdam [u.a.] : Elsevier Science, 2022) Gebhardt, Ulrike; Gustmann, Tobias; Giebeler, Lars; Hirsch, Franz; Hufenbach, Julia Kristin; Kästner, Markus
    Additive manufacturing overcomes the restrictions of classical manufacturing methods and enables the production of near-net-shaped, complex geometries. In that context, lattice structures are of high interest due to their superior weight reduction potential. AlSi10Mg is a well-known alloy for additive manufacturing and well suited for such applications due to its high strength to material density ratio. It has been selected in this study for producing bulk material and complex geometries of a strut-based lattice type (rhombic dodecahedron). A detailed characterisation of as-built and heat-treated specimens has been conducted including microstructural analyses, identification of imperfections and rigorous mechanical testing under different load conditions. An isotropic elastic–plastic material model is deduced on the basis of tension test results of bulk material test specimens. Performed experiments under compression, shear, torsion and tension load are compared to their virtual equivalents. With the help of numerical modelling, the overall structural behaviour was simulated using the detailed lattice geometry and was successfully predicted by the presented numerical models. The discussion of the limits of this approach aims to evaluate the potential of the numerical assessment in the modelling of the properties for novel lightweight structures.
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    Structure-property relationships of imperfect additively manufactured lattices based on triply periodic minimal surfaces
    (Amsterdam [u.a.] : Elsevier Science, 2022) Günther, Fabian; Hirsch, Franz; Pilz, Stefan; Wagner, Markus; Gebert, Annett; Kästner, Markus; Zimmermann, Martina
    Lattices based on triply periodic minimal surfaces (TPMS) have recently attracted increasing interest, but their additive manufacturing (AM) is fraught with imperfections that compromise their structural integrity. Initial research has addressed the influence of process-induced imperfections in lattices, but so far numerical work for TPMS lattices is insufficient. Therefore, in the present study, the structure–property relationships of TPMS lattices, including their imperfections, are investigated experimentally and numerically. The main focus is on a biomimetic Schoen I-WP network lattice made of laser powder bed fusion (LPBF) processed Ti-42Nb designed for bone tissue engineering (BTE). The lattice is scanned by computed tomography (CT) and its as-built morphology is examined before a modeling procedure for artificial reconstruction is developed. The structure–property relationships are analyzed by experimental and numerical compression tests. An anisotropic elastoplastic material model is parameterized for finite element analyses (FEA). The numerical results indicates that the reconstruction of the as-built morphology decisively improves the prediction accuracy compared to the ideal design. This work highlights the central importance of process-related imperfections for the structure–property relationships of TPMS lattices and proposes a modeling procedure to capture their implications.