Coupling effects of multiple strengthening mechanisms in spheroidal microstructure eutectic high-entropy alloys

The yield strength of metallic materials is commonly estimated using linear superposition methods, but this method has limited adaptability to new materials. The mapping relationship between input and output in machine learning methods is difficult to quantify. This study employs a rapid screening method to identify the key strengthening mechanisms that influence the yield strength of typical homogeneous and heterogeneous metallic materials. A non-linear physical neural information network model is developed to describe the yield strength of heterogeneous structures, capturing the main parameters contributing to yield strength from selected physical quantity data. By considering the non-linear strengthening mechanisms arising from interactions among multiple strengthening mechanisms in heterogeneous metal structures, the microstructure and mechanical properties of eutectic high-entropy alloys are optimized through warm rolling and short-time annealing. By inducing microstructural spheroidization and introducing nanoscale precipitates, significant coupling effects of multiple strengthening mechanisms are achieved for eutectic high-entropy alloys. As a result, the yield strength increases by approximately 2.5 times, while plasticity remains nearly unchanged. Transmission electron microscopy, scanning electron microscopy, and electron backscatter diffraction observations provide evidence for the sources of these coupling effects. This study offers guidance for accelerating material design and exploring the physical relationship between microstructure and mechanical properties.