Magnesium (Mg) alloys are highly valued in aerospace, biomedical and other fields due to their high specific strength. However, non-uniform corrosion failure during service remains a core challenge that restricts their engineering applications. Traditional corrosion kinetics models fail to accurately elucidate the cross-scale synergy mechanism between microstructure and macroscopic corrosion behavior. In this study, based on 13 kinds of Mg alloys, 20 sets of 100-h hydrogen evolution curves, and characterization data from scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) information, a multi-level corrosion kinetics database was constructed, covering physicochemical parameters, micro-grain topological structures and second phase features, as well as macroscopic statistical characteristics and temporal dimension. Through machine learning algorithms, key corrosion driving factors were identified, and a multi-level graph attention network modeling framework was proposed, where the grains and grain boundaries were constructed as a graph structure, and the hierarchical interaction modeling between microstructure and corrosion kinetics was realized by combining the attention mechanism. The model has been validated in a new Mg alloy dataset for its predictive capability across compositional systems. This work provides a new computational paradigm and significantly enhances the predictability and efficiency of corrosion-resistant Mg alloy design.

The application of machine learning in alloy design is increasingly widespread, yet traditional models still face challenges when dealing with limited datasets and complex nonlinear relationships. This work proposes an interpretable machine learning method based on data augmentation and reconstruction, excavating high-performance low-alloyed magnesium (Mg) alloys. The data augmentation technique expands the original dataset through Gaussian noise. The data reconstruction method reorganizes and transforms the original data to extract more representative features, significantly improving the model’s generalization ability and prediction accuracy, with a coefficient of determination (R²) of 95.9 % for the ultimate tensile strength (UTS) model and a R² of 95.3 % for the elongation-to-failure (EL) model. The correlation coefficient assisted screening (CCAS) method is proposed to filter low-alloyed target alloys. A new Mg-2.2Mn-0.4Zn-0.2Al-0.2Ca (MZAX2000, wt%) alloy is designed and extruded into bar at given processing parameters, achieving room-temperature strength-ductility synergy showing an excellent UTS of 395 MPa and a high EL of 17.9 %. This is closely related to its hetero-structured characteristic in the as-extruded MZAX2000 alloy consisting of coarse grains (16 %), fine grains (75 %), and fiber regions (9 %). Therefore, this work offers new insights into optimizing alloy compositions and processing parameters for attaining new high strong and ductile low-alloyed Mg alloys.

In recent years, modification of texture distribution has been considered a valid approach to improve the room-temperature (RT) formability of magnesium (Mg) alloys. In this study, a novel Mg–2Zn–3Li–1Gd alloy sheet with weak elliptical-texture was fabricated by cold rolling and subsequent annealing, and it showed an excellent Erichsen (IE) value near 7.1 mm. Both quasi-in-situ electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) analysis indicate that considerable basal and pyramidal dislocations can be activated in the cold rolling process. During annealing, these dislocations can induce nucleation and then cause preferential misorientation relationships around 〈uvt0〉 concerning the nuclei and parent grains, which can facilitate the formation of elliptical texture. Furthermore, the particle-stimulated nucleation (PSN) mechanism and the co-segregation of Zn and Gd at grain boundaries (GB) further weak texture intensity. Finally, the mechanical properties of the Mg–2Zn–3Li–1Gd alloy sheet are significantly improved.

The fundamental research on thermo-mechanical conditions provides an experimental basis for high-performance Mg-Al-Ca-Mn alloys. However, there is a lack of systematical investigation for this series alloys on the hot-deformation kinetics and extrusion parameter optimization. Here, the flow behavior, constitutive model, dynamic recrystallization (DRX) kinetic model and processing map of a dilute rare-earth free Mg-1.3Al-0.4Ca-0.4Mn (AXM100, wt.%) alloy were studied under different hot-compressive conditions. In addition, the extrusion parameter optimization of this alloy was performed based on the hot-processing map. The results showed that the conventional Arrhenius-type strain-related constitutive model only worked well for the flow curves at high temperatures and low strain rates. In comparison, using the machine learning assisted model (support vector regression, SVR) could effectively improve the accuracy between the predicted and experimental values. The DRX kinetic model was established, and a typical necklace-shaped structure preferentially occurred at the original grain boundaries and the second phases. The DRX nucleation weakened the texture intensity, and the further growth caused the more scattered basal texture. The hot-processing maps at different strains were also measured and the optimal hot-processing range could be confirmed at the deformation temperatures of 600~723 K and the strain rates of 0.018~0.563 s⁻¹. Based on the optimum hot-processing range, a suitable extrusion parameter was considered as 603 K and 0.1 mm/s and the as-extruded alloy in this parameter exhibited a good strength-ductility synergy (yield strength of ~ 232.1 MPa, ultimate strength of ~ 278.2 MPa and elongation-to-failure of ~ 20.1%). Finally, the strengthening-plasticizing mechanisms and the relationships between the DRXed grain size, yield strength and extrusion parameters were analyzed.

Designing and developing the Mg alloys with low cost and high performance is of the great significance. Novel Mg-1Bi-xMn (x = 0, 1 and 2 wt.%) extruded alloys, in this work, were fabricated at different extrusion temperatures (220, 250 and 300 ℃). The effects of extrusion temperature and Mn addition on the microstructures and mechanical properties of extruded alloys at room temperature were investigated. The results showed that decreasing the extrusion temperature could refine the average grain size, weaken the basal fiber texture intensity and improve the microstructural homogeneity of extruded alloys. When the Mn element was added to the Mg-1Bi alloy, the average grain size further reduced. Simultaneously, the number fraction of low angle grain boundaries (LAGBs) increased, along with the occurrence of regions without dynamic recrystallization (unDRX). The combined effects of grain refinement and coarse unDRXed structure made the textures of the extruded Mg-1Bi-xMn alloys never obviously change. Besides few large size un-dissolved second phases, fine Mg₃Bi₂ and α-Mn phases were precipitated in the extruded Mg-1Bi-xMn alloys and partial nano-scale α-Mn particles pined at grain boundaries (GBs) to effectively impede the migration of GBs for grain refinement. Microstructural variations determined the extruded Mg-1Bi-2Mn alloy to exhibit the highest yield strength of ~ 319.2 MPa with the appropriate elongation-to-failure of ~ 13% at the extrusion temperature of 220 ℃, and they enabled the extruded Mg-1Bi-1Mn alloy to show the highest elongation-to-failure of ~ 26% without the obvious loss of yield strength of ~ 252.1 MPa.

In this work, pre-strain annealing strengthening (PSAS) effect was investigated in an extruded Mg-1.0Gd-1.5 Zn (wt.%) alloy with respect to different grain sizes. The evolution of microstructures was provided by scanning electron microscopy (SEM), electron backscattered diffraction (EBSD), transmission electron microscopy (TEM) and high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) under the initial state, pre-compression, intermediate annealing and re-compression conditions. The obtained results showed a grain size-dependent PSAS effect in the alloy. The sample with larger grain sizes corresponded to a higher strengthening effect, which mainly resulted from a more remarkable hindrance for the growth of existing twins and a larger proportion of activation for the nucleation of new twins. This was closely associated with the increase of back stress and friction stress for twin boundary motion impeded by the larger restraint of dislocations, the higher stress field surrounding solutes and the more Zn segregation. In addition to twinning behavior, Guinier Preston (G.P.) zones on basal 〈a〉 dislocations were found after intermediate annealing and provided an extra strengthening by inhibiting the motions of gilding pre-existing dislocations and newly formed ones, but it was independent on the grain size.