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

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Now showing 1 - 3 of 3
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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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    Direct observation of nanocrystal-induced enhancement of tensile ductility in a metallic glass composite
    (Amsterdam [u.a.] : Elsevier Science, 2021) Gammer, Christoph; Rentenberger, Christian; Beitelschmidt, Denise; Minor, Andrew M.; Eckert, Jürgen
    Bulk metallic glasses (BMGs) have attracted wide interest, but their successful application is hindered by their low ductility at room temperature. Therefore, the use of composites of a BMG matrix with crystalline secondary phases has been proposed to overcome this drawback. In the present work we demonstrate the fabrication of a tailored BMG nanocomposite containing a high density of monodisperse nanocrystals with a size of around 20 nm using a combination of mechanical and thermal treatment of Cu36Zr48Al8Ag8 well below the crystallization temperature. Direct observations of the interaction of the nanocrystals with a shear band during in situ deformation in a transmission electron microscope demonstrate that the achieved nanocomposite has the potential to inhibit catastrophic fracture in tension. This demonstrates that a sufficient number of nanoscale structural heterogeneities can be a route towards BMG composites with superior mechanical properties.
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    Serrated flow of CuZr-based bulk metallic glasses probed by nanoindentation: Role of the activation barrier, size and distribution of shear transformation zones
    (Amsterdam [u.a.] : Elsevier Science, 2017) Limbach, R.; Kosiba, K.; Pauly, S.; Kühn, U.; Wondraczek, L.
    We report on the effect of Al and Co alloying in vitreous Cu50Zr50 on local deformation and serrated flow as a model for relating the size and localization of shear transformation zones (STZ) to Poisson ratio and strain-rate sensitivity of metallic glasses. Alloying with Al results in significant variations in mechanical performance, in particular, in Young's modulus, hardness and strain-rate sensitivity. Increasing strain-rate sensitivity with increasing degree of alloying indicates a reduced tendency for shear localization. In parallel, a gradual transition from inhomogeneous to homogeneous plastic flow is observed. Using a statistical analysis of the shear stress associated with the initiation of the first pop-in in the load-displacement curve during spherical indentation, the activation volume for plastic flow at the onset of yielding is reported. This analysis is employed for experimental evaluation of the compositional dependence of activation barrier, size and distribution of STZs. It is demonstrated that the STZ size does not change significantly upon Al alloying and encompasses a local volume of around 22–24 atoms. However, the barrier energy density for the initiation of a single STZ progressively increases. The broader distribution of STZs impedes their accumulation into larger-size flow units, leading to a lower number and reduced size of serrations in the load-displacement curve. On the contrary, lower barrier energy densities enable a larger quantity of STZs to be activated simultaneously. These STZs can easily percolate into large flow units, promoting plastic flow through their interaction. We employ Poisson's ratio as an indicator for plasticity to shown that this interpretation can be transferred to other types of metallic glasses. That is, larger flow units were found for metallic glasses with higher Poisson ratio and more pronounced plasticity, while the flow units in alloys with very low Poisson ratio and high brittleness are significantly reduced in size and more homogeneously distributed throughout the material.