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DDC: 530 × Subject: Additive manufacturing ×

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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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    Additive Manufacturing of Binary Ni–Ti Shape Memory Alloys Using Electron Beam Powder Bed Fusion: Functional Reversibility Through Minor Alloy Modification and Carbide Formation
    ([Cham] : Springer International Publishing, 2022) Krooß, P.; Lauhoff, C.; Gustmann, T.; Gemming, T.; Sobrero, C.; Ewald, F.; Brenne, F.; Arold, T.; Nematolahi, M.; Elahinia, M.; Thielsch, J.; Hufenbach, J.; Niendorf, T.
    Shape memory alloys (SMAs), such as Ni–Ti, are promising candidates for actuation and damping applications. Although processing of Ni–Ti bulk materials is challenging, well-established processing routes (i.e. casting, forging, wire drawing, laser cutting) enabled application in several niche applications, e.g. in the medical sector. Additive manufacturing, also referred to as 4D-printing in this case, is known to be highly interesting for the fabrication of SMAs in order to produce near-net-shaped actuators and dampers. The present study investigated the impact of electron beam powder bed fusion (PBF-EB/M) on the functional properties of C-rich Ni50.9Ti49.1 alloy. The results revealed a significant loss of Ni during PBF-EB/M processing. Process microstructure property relationships are discussed in view of the applied master alloy and powder processing route, i.e. vacuum induction-melting inert gas atomization (VIGA). Relatively high amounts of TiC, being already present in the master alloy and powder feedstock, are finely dispersed in the matrix upon PBF-EB/M. This leads to a local change in the chemical composition (depletion of Ti) and a pronounced shift of the transformation temperatures. Despite the high TiC content, superelastic testing revealed a good shape recovery and, thus, a negligible degradation in both, the as-built and the heat-treated state.