Page 80 - Read Online

Cheng et al. J. Mater. Inf. 2025, 5, 53 https://dx.doi.org/10.20517/jmi.2025.61 Page 13 of 17

Table 6. Prediction of the compositions, process parameters, and mechanical properties of high-performance Mg-Gd-based alloys
from the inverse design model
Composition (wt.%) Processing parameters Predicted Target
Alloy
Gd Y Zn Mn ST (°C) St (h) ET (°C) ER UTS/MPa EL/% UTS/MPa EL/%
VW126 11.5 6.0 1.1 0.2 512 12 400 13 438.7 6.8 450 5
VZ31 2.5 0.0 0.9 0.0 547 13 410 28 250.1 40.0 250 40

ST: Solid solution temperature; St: solid solution time; ET: extrusion temperature; ER: extrusion ratio; UTS: ultimate tensile strength; EL: elongation.

Table 7. Chemical composition and processing parameters applied practically in the experiments
Composition (wt.%) Processing parameters
Alloy
Gd Y Zn Mn ST (°C) St (h) ET (°C) ER
VW126 11.4 6.2 1.2 0.3 515 12 400 10
VZ31 2.7 0.0 0.9 0.0 545 13 410 25

ST: Solid solution temperature; St: solid solution time; ET: extrusion temperature; ER: extrusion ratio.

Figure 8. The Pareto front of high-performance Mg-Gd-based alloys generated by the RF-NSGA inverse design model: (A) high-
strength alloy with the target UTS = 450 MPa and EL = 5%; and (B) high-ductility alloy with the target UTS = 250 MPa and EL = 40%.
RF: Random forest; NSGA: non-dominated sorting genetic algorithm; UTS: ultimate tensile strength; EL: elongation.

Figure 9. Room-temperature tensile stress-strain curves of high-performance Mg-Gd-based alloys designed by the inverse design
model: (A) VW126 alloy and (B) VZ31 alloy.