Ductile W0.4MoNbxTaTi refractory high-entropy alloys with excellent elevated temperature strength

2021 ◽  
Vol 295 ◽  
pp. 129753
Author(s):  
Wenchao Wei ◽  
Tao Wang ◽  
Chunyu Wang ◽  
Meng Wu ◽  
Yunpeng Nie ◽  
...  
Wear ◽  
2021 ◽  
pp. 204166
Author(s):  
Xuhui Pei ◽  
Yin Du ◽  
Xiaoxiao Hao ◽  
Hanming Wang ◽  
Qing Zhou ◽  
...  

2013 ◽  
Vol 80 (1-2) ◽  
pp. 177-190 ◽  
Author(s):  
Ömer N. Doğan ◽  
Benjamin C. Nielsen ◽  
Jeffrey A. Hawk

MRS Bulletin ◽  
2019 ◽  
Vol 44 (11) ◽  
pp. 860-866 ◽  
Author(s):  
P.H. Lin ◽  
H.S. Chou ◽  
J.C. Huang ◽  
W.S. Chuang ◽  
J.S.C. Jang ◽  
...  

Abstract


2021 ◽  
Author(s):  
Shailesh Kumar Singh ◽  
Vivek K. Singh

The conventional design approach of alloys initiates with one principal element and continues by adding several alloying elements to obtain desired properties. In this method, the intrinsic properties of the designed alloy are governed by the principal element. For example, in steel alloy, iron is the principal element, Aluminium in aluminium alloy, and so on. Compared to the conventional alloy, high entropy alloys do not have any dominating elements; all the elements present in these alloys either have an equal or near-equal ratio of elements. As reported in the literature, these alloys exhibit interesting material properties such as high strength, high hardness, improved elevated temperature strength, and magnetic properties. These characteristics make HEAs a suitable option for high-performance applications in the aero engine, aerospace structures, and machine tools. High entropy alloy has multiple principal elements as shown in schematic diagram 1; it leads to much higher possible compositions than conventional alloys. The huge compositional space provides an opportunity to improve desired mechanical properties. If it is explored through “trial and error,” it will be challenging and cumbersome. Therefore, search schemes that can competently and promptly recognize particular alloys with desired properties are essential. Artificial Intelligence is a useful tool to model, discover, and optimize new alloys that enable predicting individual material properties as a function of composition. While the application of Artificial Intelligence is quite popular in many aspects of society, its usage in material informatics is still in the nascent stage. The algorithm used in artificial intelligence is trained to pick up predictive rules from data and create a material model quicker than a computational model and can even generate the model for which no physical model exists. Artificial Intelligence (AI) allows predicting a set of experiments to be conducted to detect new alloy having desired properties. Thus, AI can be used as a valuable tool to optimize the development of new alloys.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sheng Yin ◽  
Yunxing Zuo ◽  
Anas Abu-Odeh ◽  
Hui Zheng ◽  
Xiang-Guo Li ◽  
...  

AbstractRefractory high-entropy alloys (RHEAs) are designed for high elevated-temperature strength, with both edge and screw dislocations playing an important role for plastic deformation. However, they can also display a significant energetic driving force for chemical short-range ordering (SRO). Here, we investigate mechanisms underlying the mobilities of screw and edge dislocations in the body-centered cubic MoNbTaW RHEA over a wide temperature range using extensive molecular dynamics simulations based on a highly-accurate machine-learning interatomic potential. Further, we specifically evaluate how these mechanisms are affected by the presence of SRO. The mobility of edge dislocations is found to be enhanced by the presence of SRO, whereas the rate of double-kink nucleation in the motion of screw dislocations is reduced, although this influence of SRO appears to be attenuated at increasing temperature. Independent of the presence of SRO, a cross-slip locking mechanism is observed for the motion of screws, which provides for extra strengthening for refractory high-entropy alloy system.


Author(s):  
R. E. Franck ◽  
J. A. Hawk ◽  
G. J. Shiflet

Rapid solidification processing (RSP) is one method of producing high strength aluminum alloys for elevated temperature applications. Allied-Signal, Inc. has produced an Al-12.4 Fe-1.2 V-2.3 Si (composition in wt pct) alloy which possesses good microstructural stability up to 425°C. This alloy contains a high volume fraction (37 v/o) of fine nearly spherical, α-Al12(Fe, V)3Si dispersoids. The improved elevated temperature strength and stability of this alloy is due to the slower dispersoid coarsening rate of the silicide particles. Additionally, the high v/o of second phase particles should inhibit recrystallization and grain growth, and thus reduce any loss in strength due to long term, high temperature annealing.The focus of this research is to investigate microstructural changes induced by long term, high temperature static annealing heat-treatments. Annealing treatments for up to 1000 hours were carried out on this alloy at 500°C, 550°C and 600°C. Particle coarsening and/or recrystallization and grain growth would be accelerated in these temperature regimes.


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