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- ItemLaser powder bed fusion of a superelastic Cu-Al-Mn shape memory alloy(Amsterdam [u.a.] : Elsevier Science, 2021) Babacan, N.; Pauly, S.; Gustmann, T.Dense and crack-free specimens of the shape memory alloy Cu71.6Al17Mn11.4 (at.%) were produced via laser powder bed fusion across a wide range of process parameters. The microstructure, viz. grain size, can be directly tailored within the process and with it the transformation temperatures (TTs) shifted to higher values by raising the energy input. The microstructure, and the superelastic behavior of additively manufactured samples were assessed by a detailed comparison with induction melted material. The precipitation of the α phase, which inhibit the martensitic transformation, were not observed in the additively manufactured samples owing to the high intrinsic cooling rates during the fabrication process. Fine columnar grains with a strong [001]-texture along the building direction lead to an enhanced yield strength compared to the coarse-grained cast samples. A maximum recoverable strain of 2.86% was observed after 5% compressive loading. The first results of our approach imply that laser powder bed fusion is a promising technique to directly produce individually designed Cu-Al-Mn shape memory parts with a pronounced superelasticity at room temperature.
- ItemControlling the Young’s modulus of a ß-type Ti-Nb alloy via strong texturing by LPBF(Amsterdam [u.a.] : Elsevier Science, 2022) Pilz, Stefan; Gustmann, Tobias; Günther, Fabian; Zimmermann, Martina; Kühn, Uta; Gebert, AnnettThe ß-type Ti-42Nb alloy was processed by laser powder bed fusion (LPBF) with an infrared top hat laser configuration aiming to control the Young’s modulus by creating an adapted crystallographic texture. Utilizing a top hat laser, a microstructure with a strong 〈0 0 1〉 texture parallel to the building direction and highly elongated grains was generated. This microstructure results in a strong anisotropy of the Young’s modulus that was modeled based on the single crystal elastic tensor and the experimental texture data. Tensile tests along selected loading directions were conducted to study the mechanical anisotropy and showed a good correlation with the modeled data. A Young’s modulus as low as 44 GPa was measured parallel to the building direction, which corresponds to a significant reduction of over 30% compared to the Young’s modulus of the Gaussian reference samples (67–69 GPa). At the same time a high 0.2% yield strength of 674 MPa was retained. The results reveal the high potential of LPBF processing utilizing a top hat laser configuration to fabricate patient-specific implants with an adapted low Young’s modulus along the main loading direction and a tailored mechanical biofunctionality.
- ItemLaser 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, KonradIn 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.
- 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.