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High Entropy Superalloy (HESA) is a classification of materials with promising properties extensively developed to improve performance, resource sustainability, and cost efficiency in high-temperature applications. The need for computation on HESA is due to its time and cost superiority over experiments while maintaining good accuracy. However, thermodynamic data of some rare elements like Zr has not existed in publications based on a phase diagram calculation. First-principles is then used to investigate further the effect of decreasing Ni while adding Cu and Zr on lattice parameters, elasticity, stacking fault energy (SFE), and electronic structure of Fe-based HESA FeNiCrAlCo and FeNiCrAlCu. Adding Cu increases the SFE and ductility while decreasing Ni and adding Zr decreases the SFE and increases the strength but slightly reduces the ductility. Electronic structure analysis showed that adding Zr increases charge accumulation and decreases density of states, then interatomic bonding weakened, interlayer distance increased, and SFE decreased. The Fe-based HESA design can be optimized by reducing Ni concentration and increasing Cr concentration to decrease SFE to significantly increase strength, ductility, and hardness, especially at high temperatures, or adding Zr to decrease SFE value to the maximum. This study will help develop low-cost high entropy superalloys with desired performance.

High entropy superalloys (HESA) have great potential to replace superalloys with promising properties extensively developed to improve performance, resource sustainability, and cost efficiency in high-temperature applications. This study focuses on Fe-based HESA and their stacking fault energy (SFE), a critical parameter influencing deformation mechanism and creep resistance. This development is economically cheaper since it utilizes Fe rather than Ni as the alloy base, which has been widely developed. We propose a novel approach for predicting SFE using big data analysis leveraging machine learning and computational thermodynamics. The calculated SFE as a function of compositions and temperature becomes the database for the machine learning model. We employ a deep learning neural network model to achieve an impressive 0.008 Root Mean Squared Error (RMSE) predicting SFE values and classes. The composition of the high entropy superalloy is designed to lower the SFE, which promotes the formation of stacking faults and twin boundaries, resulting in high strength and creep resistance at high temperatures. Our research establishes an optimal design guide for achieving desired SFE: Ni (9-15 at%), Cr (15-36 at%), Al (5-22.75 at%), Cu (9-22.75 at%), and Fe (22.75-40 at%). Fe can be increased until 40 at.% with 15 at.% Ni, or Ni can be reduced until 9 at.% with a lower Fe of 22.75 at.%.

The development of high-entropy alloys (HEAs) for high-temperature applications has been driven by the limitation of nickel-based superalloys in achieving optimal efficiency at higher temperatures for higher efficiency in power generation engines. The alloys must have high oxidation resistance and microstructural stability at high temperatures. Relatively equimolar multi elements involved in HEAs produce microstructure containing a single solid solution or multiphase that improves the mechanical properties and oxidation resistance resulting from sluggish diffusion and core effects. In this study, the oxidation behavior and microstructural changes of Al0.75CoCrFeNi HEA at 900, 1000, and 1100 °C in air atmosphere were investigated. Based on the XRD and SEM-EDS analysis, the mechanism of oxide scale formation and microstructural changes of the substrate are proposed. The results show that the oxidation behavior of the alloy follows a logarithmic rate law. Different oxide compounds of CoO, NiO, Cr2O3, and CrO3, θ-Al2O3, α-Al2O3, and Ni(Cr,Al)2O4 with semicontinuous oxides of Al2O3 with Cr2O3 subscale and an oxide mixture consisting of spinel of Ni(Cr,Al)2O4 and Co(Cr,Al)2O4 were found. During oxidation, Widmanstätten of FCC-A1 and BCC-B2/A2 phases in the substrate have changed. Spheroidization of B2 and a reduction in volume fraction decrease the hardness of the substrates. [ABSTRACT FROM AUTHOR]

The generalized-stacking-fault-energy (GSFE) curve for pure Cobalt and Co-9 at.% X solid-solution alloys (X = Cr, W, Mo, Ni, Mn, Al, Fe) have been successfully calculated by rigidly shearing an fcc crystal at a (1 1 1) plane along a [1 1 −2] slip direction in two processes using first-principles density-functional-theory (DFT), provide a highly accurate modeling and a fairly good agreement with available experimental and other theoretical results in the literature. The objectives of this study are to provide a useful guideline for the Co-based alloy design with superior performances and new insight for understanding the nature of the fcc to hcp phase transformation. Evaluation of the GSFE values is based on the ratio of the stable and unstable stacking fault energy, the twinnabilities with three criterions for crack tip twinning, grain boundary twinning and inherent twinning, as well as the predicted critical twinning stress. It concludes that alloying with Cr, W and Mo atoms are expected to bring a significant increase in the tendency of the dominant deformation mechanism for the formation of partial dislocations and mechanical twinning of pure Co. The investigation regarding the electronic structure, such as interlayer distance distortion between a faulted and perfect fcc structure, atomic bonding, charge density distributions and density of states (DOS) in the present work can describe why and how alloying atoms change the GSFE values of Co-based alloys. [ABSTRACT FROM AUTHOR]