Tailoring nanoprecipitates for ultra-strong high-entropy alloys via machine learning and prestrain aging

doi:10.1016/j.jmst.2020.07.009

Abstract

Abstract:

The multi-principal-component concept of high-entropy alloys (HEAs) generates numerous new alloys. Among them, nanoscale precipitated HEAs have achieved superior mechanical properties and shown the potentials for structural applications. However, it is still a great challenge to find the optimal alloy within the numerous candidates. Up to now, the reported nanoprecipitated HEAs are mainly designed by a trial-and-error approach with the aid of phase diagram calculations, limiting the development of structural HEAs. In the current work, a novel method is proposed to accelerate the development of ultra-strong nanoprecipitated HEAs. With the guidance of physical metallurgy, the volume fraction of the required nanoprecipitates is designed from a machine learning of big data with thermodynamic foundation while the morphology of precipitates is kinetically tailored by prestrain aging. As a proof-of-principle study, an HEA with superior strength and ductility has been designed and systematically investigated. The newly developed γ′-strengthened HEA exhibits 1.31 GPa yield strength, 1.65 GPa ultimate tensile strength, and 15% tensile elongation. Atom probe tomography and transmission electron microscope characterizations reveal the well-controlled high γ′ volume fraction (52%) and refined precipitate size (19 nm). The refinement of nanoprecipitates originates from the accelerated nucleation of the γ′ phase by prestrain aging. A deeper understanding of the excellent mechanical properties is illustrated from the aspect of strengthening mechanisms. Finally, the versatility of the current design strategy to other precipitation-hardened alloys is discussed.

Key words: High-entropy alloys, Machine learning, Prestrain aging, Mechanical properties, Strengthening mechanisms

Tao Zheng, Xiaobing Hu, Feng He, Qingfeng Wu, Bin Han, Chen Da, Junjie Li, Zhijun Wang, Jincheng Wang, Ji-jung Kai, Zhenhai Xia, C.T. Liu. Tailoring nanoprecipitates for ultra-strong high-entropy alloys via machine learning and prestrain aging[J]. J. Mater. Sci. Technol., 2021, 69: 156-167.

Figures/Tables 10

Fig. 1. Schematics of the current alloy design strategy based on ML: (a) The general procedure of ML in this work; (b) The network structure of ANN for the selection of alloys satisfying the target properties.

Fig. 2. Performance evaluation of the ANN model learning (regression analysis). (a) Training of γ′ phase volume fraction; (b) Testing of γ′ phase volume fraction, the red diamonds represent the testing results of γ′ phase volume fraction of several published HEAs data [17], [28], [29], [30][; (c) Training of yield strength; (d) Testing of yield strength. The R and C in each diagram represent the slope and intercept of the fitting curve, respectively.

Fig. 3. Frequency maps of the filtered 452 alloys composition; (a)-(f) are the frequency maps of Ni, Co, Fe, Cr, Al and Ti, respectively.

Fig. 4. The interaction statistics between every two elements. The pink dots represent the corresponding alloy composition. Some dots may coincide, and the depth of dots color is related to the number of alloy composition located.

Fig. 5. XRD and SEM micrographs of the designed HEA. The left graphs (a), (c) and (e) show the XRD pattern, fine grains, and dispersed nanoprecipitates of the as-aged specimen, respectively, while the right graphs (b), (d) and (f) show the counterparts of prestrain-aged sample.

Fig. 6. TEM images showing the nanoprecipitates and crystal structures. (a) and (b) are the dark-field morphology of nanoprecipitates in as-aged and prestrain-aged specimens, respectively. (c) and (d) are statistical size distribution of nanoprecipitates in as-aged alloy and prestrain-aged alloy, respectively. (e) SADP patterns along three different zone axes from prestrain-aged sample. (f) HRTEM image revealing the interface between nanoprecipitate and FCC matrix with two FFT patterns of γ′ nanoprecipitate and FCC matrix adhered to the right.

Fig. 7. APT reconstruction map and atom map at a 50 at.% Ni iso-concentration surface of prestrain-aged alloy: (a) Reconstruction map; (b) Curves of concentration profile; (c) Atom maps of six elements.

Fig. 8. Mechanical properties and fracture morphology of the designed alloy: (a) Tensile engineering stress-strain curves of different fabricating processes; (b) Evolution of ultimate tensile strength versus fracture elongation during different process steps; (c) and (d) the fracture surface morphology of as-aged and prestrain-aged specimens, respectively.

Table 1 Concentration of elements in prestrain-aged alloy measured by APT (at.%), errors are obtained by averaging different particles.

Phase	Ni	Co	Fe	Cr	Al	Ti
matrix	17.09 ± 0.19	30.08 ± 0.23	39.45 ± 0.27	7.68 ± 0.13	4.14 ± 0.07	1.56 ± 0.04
precipitate	49.10 ± 0.46	19.16 ± 0.32	6.05 ± 0.13	1.62 ± 0.08	9.56 ± 0.21	14.51 ± 0.26

Fig. 9. (a) The contribution of different strengthening mechanisms to the yield strength; (b) and (c) the comparative results of strength and elongation with other NPH HEAs. The mechanical properties are obtained from Co9Cr7Cu36Mn25Ni23 [51], CoCrCuMnNi [52], (Ni2Co2FeCr)92Al4Nb4 [53], Al0.2CrFeCoNi2Cu0.2 [54], Al0.3CoCrFeNi [55], Al0.096CoCrNiTi0.096 [29], Al0.17CoCrFeNiTi0.09 [10], Ni56.4-xCoxFe18.8Cr18.8Ti6 [30], FeNiCrMn-(Al, Ti)0.6-1.7 [56], (FeCoCr)40Ni40Al10Cu10 [57], Al0.2Co1.5CrFeNi1.5Ti0.3 [36], Al0.6CoCrFeMnNi [34], Al0.3CoCrFeNi [58], Al0.5CoCrFeNi [59], (CoCrFeNi)94-xAl3Ti3Nbx [60], and Al0.5Cr0.9FeNi2.5V0.2 [11].

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