High-entropy alloys (HEAs) with ultrafine grained and high strength can be prepared by mechanical alloying (MA) followed by sintering. Therefore, MA, as a unique solid powder processing method, has many effects on the microstructures and mechanical properties of the sintered bulk HEAs. This work focused on the alloying behavior, morphology, and phase evolution of Fe_xCrNiAl (x = 1.0, 0.5, 0.25) HEAs by MA. The X-ray diffraction results show that the powders achieved a supersaturated solid solution body-centered-cubic (BCC) phase after MA; the crystalline size reached the nanoscale and was refined to ~80 nm. The morphology and composition of the alloyed powders were studied by scanning electron microscopy with energy dispersive spectroscopy. The results indicate that the powder was decreased to 1.59 μm for Fe_1.0 powder with excellent homogeneity in composition. There exists a phase transformation during high-temperature annealing, as the non-equilibrium BCC supersaturated solid solution phase transformed into the equilibrium phase of BCC and ordered BCC (B2) phases.

The FeNiCrAlCoCuTi alloy system has great advantages in mechanical properties such as high hardness and toughness. It has high performance potential and research value and the key in research is designing alloy compositions with target properties. The traditional method, experimental analysis, is highly inefficient to properly exploit the intrinsic relationship between material characteristics and properties for multi-component alloys, especially in investigating the whole composition space. In this work, we present a research way that uses first principles calculation to obtain the properties of multi-component alloys and uses machine learning to accelerate the research. The FeNiCrAlCoCuTi alloy system with its elastic properties is used as an example to demonstrate this process. We specifically design models for each output, all of which have RMSE values of less than 1.1, and confirm their effectiveness through experimental data in the literature, showing that the relative error is below 5%. Additionally, we perform an interpretable analysis on the models, exposing the underlying relationship between input features and output. By means of spatial transformation, we achieve the prediction of the full-component spatial performance from binary to multiple components. Taking the FeNiCrAlM (M = Co, Cu, Ti) quinary alloy system as an example, we design a single-phase BCC structure composed of an Fe_0.23Cr_0.23Al_0.23Ni_0.03Cu_0.28 alloy with a Young’s modulus of 273.10 GPa, as well as a single-phase BCC structure composed of an Fe_0.01Cr_0.01Al_0.01Ni_0.44Co_0.53 alloy with a shear modulus of 103.6 GPa. Through this research way, we use machine learning to accelerate the calculation, which greatly shortens research time and costs. This work overcomes the drawbacks of traditional experiments and directly obtains element compositions and composition intervals with excellent performance.