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Author: Kosiba, Konrad × 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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    Temperature-dependent dynamic compressive properties and failure mechanisms of the additively manufactured CoCrFeMnNi high entropy alloy
    (Oxford : Elsevier Science, 2022) Chen, Hongyu; Liu, Yang; Wang, Yonggang; Li, Zhiguo; Wang, Di; Kosiba, Konrad
    CoCrFeMnNi high entropy alloy (HEA) parts were fabricated by laser powder bed fusion (LPBF), and their dynamic compressive properties at different temperatures as well as the resulting microstructures were analyzed. The HEAs showed an unprecedented strength-ductility combination, especially at a cryogenic temperature of 77 K and a high strain rate of 3000 s−1. Under this testing condition, the yield strength (YS) of the HEAs amounted to 665 MPa. Regardless of the testing temperature, the deformation mechanism of all investigated HEAs was dominated by a synergistic effect consisting of deformation twinning and dislocation pile-up around twins. The fraction of twin boundaries and dislocation density within the deformed microstructure of the HEA correlated with the test temperature. At 77 K, the formation of nanotwins together with dislocation slip prevailed and contributed to pronounced twin-twin and twin-dislocation interactions which effectively restricted the dislocation movement and, hence, contributed to a higher YS as well as strain hardening rate in comparison to that of the HEAs at room temperature of 298 K. The LPBF-fabricated HEAs showed unpronounced thermal softening even at a high testing temperature of 1073 K. Continuous dynamic recrystallization was restricted in the HEA because of its inherent sluggish dislocation kinetics and low stacking fault energy.
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    Effect of scanning strategy on microstructure and mechanical properties of a biocompatible Ti–35Nb–7Zr–5Ta alloy processed by laser-powder bed fusion
    (Berlin : Springer, 2022) Batalha, Weverson Capute; Batalha, Rodolfo Lisboa; Kosiba, Konrad; Kiminami, Claudio Shyinti; Gargarella, Piter
    The influence of scanning strategy (SS) on microstructure and mechanical properties of a Ti–35Nb–7Zr–5Ta alloy processed by laser-powder bed fusion (L-PBF) is investigated for the first time. Three SSs are considered: unidirectional-Y; bi-directional with 79° rotation (R79); and chessboard (CHB). The SSs affect the type and distribution of pores. The highest relative densities and more homogeneous distribution of pores are obtained with R79 and CHB scanning strategies, whereas aligned pores are formed in the unidirectional-Y. The SSs show direct influence on the crystallographic texture with unidirectional-Y strategy showing fiber texture. The R79 strategy results in a weak texture and the CHB scanning strategy forms a randomly oriented heterogeneous grain structure. The lowest Young modulus is obtained with the unidirectional-Y strategy, whereas the R79 strategy results in the highest yield strength. It is shown that the SSs may be used for tuning the microstructure of a beta-Ti alloy in L-PBF.