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Author: Kosiba, Konrad × Publisher: Amsterdam [u.a.] : Elsevier Science ×

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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 Fe60(CoCrNiMn)40 medium-entropy alloy with excellent strength-ductility balance
    (Amsterdam [u.a.] : Elsevier Science, 2024) Yang, Shengze; Liu, Yang; Chen, Hongyu; Wang, Yonggang; Kosiba, Konrad
    In this study, Fe60(CoCrNiMn)40 medium-entropy alloy (MEA) was fabricated by laser powder bed fusion (LPBF) via mixing of pure Fe and FeCoCrNiMn powders, the processability, microstructure and mechanical properties were systematically investigated, and the mechanism of strengthening and toughening were revealed through combination of experiments and molecular dynamics (MD) simulations. Results show that fraction of BCC phase decreased gradually with increasing volume energy density (VED), and thus heterostructue with varying FCC and BCC phases were produced through regulating the VED. The Fe60(CoCrNiMn)40 MEA (with scanning speeds of 700 and 800 mm/s) showed excellent strength-plasticity balance (e.g. 476 MPa, 612 MPa and 63 %) compared to the equiatomic FeCoCrNiMn HEA, which is ascribed to the synergistic strengthening and toughening effects involving the twinning induced plasticity (TWIP) and the reinforcement caused by the BCC phase (act as reinforced particle) embedded in the FCC matrix.