The invention relates to an experimental data-driven multi-principal-element
alloy strength prediction and design method, which comprises the following steps:
data acquisition: measuring or collecting room-temperature tensile curves of various large-grain
alloy samples to obtain yield strength sigma y; carrying out characteristic
engineering, defining an electronegative mismatch parameter
delta x, measuring
local structure distortion parameters Uiso and epsilon 1st, and calculating to obtain an
alloy poisson ratio v; constructing a model, adopting a symbolic regression
algorithm, and inputting characteristics including
delta x, Uiso, epsilon 1st and Poisson's ratio v; the expression complexity and the fitting precision are balanced through a multi-objective optimization
algorithm, an optimal solution set is generated, and a formula with the
training set and the
test set in front, the difference is minimum and the formula complexity is not larger than 6 is selected from the optimal solution set to serve as a yield strength prediction formula; component design is conducted, the yield strength of different alloys is solved through multiple analytical formulas, and alloy components with the large product of the electronegative difference and the
shear modulus are screened out; and determining a single-phase FCC component window through
phase diagram calculation. According to the method, the component design efficiency is improved; a
quantitative design tool is provided for high-strength material development in the fields of
aerospace, nuclear energy and the like, and the research and
development period is shortened.