6082 aluminum alloys are widely applied in lightweight structural fields, but their strength levels are relatively limited, and the difficulty in synergistically enhancing strength and ductility remains a key issue restricting the expansion of their high-end applications. To address this issue, this paper proposes an integrated research strategy combining machine learning (ML)-assisted composition design with high-energy ball milling-pulse forging (HEBM-PF) non-equilibrium processing. Under the premise of satisfying national standard composition constraints, target compositions with excellent strength-ductility matching potential were screened out through an automated composition discovery framework, and further experimentally prepared using the HEBM-PF process. The results show that the ML-assisted composition design can effectively pinpoint the target composition of 6082 aluminum alloy with both high strength and good ductility potential in a multi-component space; on this basis, HEBM induced significant mechanical alloying (MA), grain refinement, and high-density defect introduction; while PF further achieved alloy densification, precipitation regulation, and non-equilibrium microstructure retention. The PF-processed alloy eventually formed a multi-scale defect non-equilibrium composite microstructure composed of an ultrafine-grained α -Al matrix, high-density dislocation tangles, stacking faults (SFs), multiple stacking faults (MSFs), nanotwins (NTs), and β -Mg₂Si and α -Al(Fe, Mn)Si nano-precipitates. Benefiting from this microstructure, the PF-processed 6082 aluminum alloy obtained a yield strength (YS) of 493 MPa, an ultimate tensile strength (UTS) of 586 MPa, and an elongation (EL) of 7.92%. Strengthening mechanism analysis indicates that dislocation strengthening, grain boundary strengthening, second-phase strengthening, and nanoscale planar defect strengthening collectively contributed to the high strength of the alloy, among which nanoscale planar defect strengthening and second-phase strengthening are particularly critical. This study provides a new pathway for the efficient design and preparation of high-performance 6082 aluminum alloys, and simultaneously provides theoretical support for the synergistic optimization of data-driven alloy design and advanced metal processing technologies.
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Zhong et al. (2026) studied this question.
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