Genetic design of new aluminum alloys to overcome strength-ductility trade-off dilemma

Abstract

In this study, machine learning and inverse design based on a genetic algorithm was used to design three aluminum wrough alloy types to overcome the strength-ductility trade-off. The composition of the new alloys was advantageous in relation to that of commercial alloys, and this was experimentally validated using samples produced by a semi-mass-production-scale process. The relationship between microstructures and mechanical properties was exploited to characterize the alloys, and each alloy exhibited different precipitation types. The major precipitate of alloy 1 was the spheroidal α-AlMnSi phase, which contributed to the Orowan mechanism. In contrast, the major precipitate of alloys 2 and 3 was the fine needle-type θ-series phase, which contributed to the dislocation shearing mechanism. The new alloys showed outstanding tensile strength (431.69, 527.03, and 527.79 MPa) without a decrease in ductility. These findings suggest that machine learning and inverse design methods are suitable for discovering new aluminum alloy types.