Investigation of aluminum concentration on stacking fault energies of hexagonal close-packed high-entropy alloys Hf0.25Ti0.25Zr0.25Sc0.25-xAlx(x<15%)

Abstract Activation of different slip systems in hexagonal close packed (h.c.p.) metals depends primarily on the c/a ratio, which is an intrinsic property that can be altered through alloying addition. In conventional h.c.p. alloys where there is no diffusion-less phase transformation and associated transformation volume change with deformation, the c/a ratio remains constant during deformation. In the present study, c/a ratio and transformation volume change of h.c.p. epsilon martensite phase in transformative high entropy alloys (HEAs) were quantified as functions of alloy chemistry, friction stir processing and tensile deformation. The study revealed that while intrinsic c/a is dependent on alloying elements, c/a of epsilon in transformative HEAs changes with processing and deformation. This is attributed to transformation volume change induced dependence of h.c.p. lattice parameters on microstructure and stress state. Lower than ideal c/a ratio promotes non-basal pyramidal 〈c + a〉 slip and deformation twinning in epsilon phase of transformative HEAs. Also, a unique twin-bridging mechanism was observed, which provided experimental evidence supporting existing theoretical predictions; i.e., geometrical factors combined with grain orientation, c/a ratio and plastic deformation can result in characteristic twin boundary inclination at 45–50°.

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Effect of ordering on stacking fault energy of VNiFeCo high entropy alloys

Materials Today Communications ◽

10.1016/j.mtcomm.2020.101336 ◽

2020 ◽

Vol 25 ◽

pp. 101336

Author(s):

Zhikai Wu ◽

Fuyang Tian

Keyword(s):

Stacking Fault ◽

Stacking Fault Energy ◽

High Entropy Alloys ◽

High Entropy

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Machine Learning Enabled Prediction of Stacking Fault Energies in Concentrated Alloys

Metals ◽

10.3390/met10081072 ◽

2020 ◽

Vol 10 (8) ◽

pp. 1072

Author(s):

Gaurav Arora ◽

Dilpuneet S. Aidhy

Keyword(s):

Machine Learning ◽

Random Distribution ◽

Nearest Neighbor ◽

Binary Alloys ◽

High Strength ◽

Stacking Fault ◽

High Entropy ◽

Distribution Of Elements ◽

Atomistic Calculations ◽

Stacking Fault Energies

Recent works have revealed a unique combination of high strength and high ductility in certain compositions of high-entropy alloys (HEAs), which is attributed to the low stacking fault energy (SFE). While atomistic calculations have been successful in predicting the SFE of pure metals, large variations up to 200 mJ/m2 have been observed in HEAs. One of the leading causes of such variations is the limited number of atoms that can be modeled in atomistic calculations; as a result, due to random distribution of elements in HEAs, various nearest neighbor environments may not be adequately captured in small supercells resulting in different SFE values. Such variation further increases with the increase in the number of elements in a given composition. In this work, we use machine learning to overcome the limitation of smaller system sizes and provide a methodology to significantly reduce the variation and uncertainty in predicting SFEs. We show that the SFE can be accurately predicted across the composition ranges in binary alloys. This capability then enables us to predict the SFE of multi-elemental alloys by training the model using only binary alloys. Consequently, SFEs of complex alloys can be predicted using a binary alloys database, and the need to perform calculations for every new composition can be circumvented.

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Generalized Stacking Fault Energy of Al-Doped CrMnFeCoNi High-Entropy Alloy

Nanomaterials ◽

10.3390/nano10010059 ◽

2019 ◽

Vol 10 (1) ◽

pp. 59 ◽

Cited By ~ 5

Author(s):

Xun Sun ◽

Hualei Zhang ◽

Wei Li ◽

Xiangdong Ding ◽

Yunzhi Wang ◽

...

Keyword(s):

Interfacial Energy ◽

Stacking Fault ◽

Stacking Fault Energy ◽

High Entropy Alloy ◽

High Entropy Alloys ◽

Face Centered Cubic ◽

High Entropy ◽

Al Content ◽

Generalized Stacking Fault ◽

Generalized Stacking Fault Energy

Using first-principles methods, we investigate the effect of Al on the generalized stacking fault energy of face-centered cubic (fcc) CrMnFeCoNi high-entropy alloy as a function of temperature. Upon Al addition or temperature increase, the intrinsic and extrinsic stacking fault energies increase, whereas the unstable stacking fault and unstable twinning fault energies decrease monotonously. The thermodynamic expression for the intrinsic stacking fault energy in combination with the theoretical Gibbs energy difference between the hexagonal close packed (hcp) and fcc lattices allows one to determine the so-called hcp-fcc interfacial energy. The results show that the interfacial energy is small and only weakly dependent on temperature and Al content. Two parameters are adopted to measure the nano-twinning ability of the present high-entropy alloys (HEAs). Both measures indicate that the twinability decreases with increasing temperature or Al content. The present study provides systematic theoretical plasticity parameters for modeling and designing high entropy alloys with specific mechanical properties.

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