The application discloses an Al-Si-Mg-Ti-Sr-
Zr alloy heat treatment optimization method based on
machine learning regulation, and belongs to the technical field of aluminum
alloy materials. The application is based on
machine learning assisted component design, adopts vacuum
medium frequency induction melting to prepare as-cast
alloy, and obtains a compact-structure and uniform-component
ingot through
vacuum pumping,
argon gas washing, grading current heating, melt shaking and mixing, and pouring cooling; then, T5 and T6 double-
system heat treatment is carried out according to GB / T 1173-2013 specification,
solid solution time, aging temperature and aging time are systematically optimized, and optimal process parameters are determined. After optimal process treatment, the
alloy realizes complete spheroidization of eutectic Si, dispersion
precipitation of (Al, Si)3(Zr, Ti) phase, and coherent strengthening of nanoscale Mg2Si phase, the maximum tensile strength can reach 341 MPa, the elongation is 5.5%, the comprehensive mechanical properties are significantly better than those of a commercial ZL101A alloy, the expensive Sc element is not used, the cost is low, the process is stable, the industrial production can be realized, and the application can be used in the field of lightweight structural parts such as automobile engine supports, gearbox housings and
chassis structural parts.