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- ItemMachine 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, KonradAdditive 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.
- ItemDirect 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, JürgenBulk 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.