The effect of Mn on the microstructure and mechanical properties of as-extruded Mg-0.5Sr alloy were discussed in this work. The results showed that high Mn alloying (2 wt.%) could significantly improve the mechanical properties of the alloys, namely, the tensile and compressive yield strength. The grain size of as-extruded Mg-0.5Sr alloys significantly was refined from 2.78 μm to 1.15 μm due to the pinning effect by fine α-Mn precipitates during the extrusion. Moreover, it also showed that the tensile yield strength and the compressive yield strength of Mg-0.5Sr-2Mn alloy were 32 and 40 percent age higher than those of Mg-0.5Sr alloy, respectively. Moreover, the strain hardening behaviors of the Mg-0.5Sr-2Mn alloy were discussed, which proved that a large number of small grains and texture have an important role in improving mechanical properties.

Mg–Al binary alloys in the concentration range from 0 to 4.0 wt.% Al have been prepared under conventional casting conditions. The as-cast Mg and Mg–Al alloys after solution treatment were processed via hot extrusion at 350 °C. The results show that Al has a positive influence on grain refinement and solution strengthening. The as-extruded Mg–Al alloys are fully recrystallized, and the tensile yield strength of the binary alloys is two times higher than that of pure Mg. Furthermore, the elongations of Mg–Al alloys are much higher than that of pure Mg. In addition, Mg and Mg–Al alloys were further studied by the viscoplastic self-consistent (VPSC) model to explore the activation and evolution of deformation modes. The simulation results match well with the experimental results.

Hot rolling was carried out in this study to modify the microstructures of an extruded Mg–6Al–1Sn–0.3Mn alloy sheet and investigate its effects on mechanical properties. After hot rolling, the grains and second phase of the extruded alloy sheet were remarkably refined, and the c-axis of a few grains was parallel to the transverse direction. The strength improvement was mainly attributed to the grain and Mg₁₇Al₁₂ particle refinement due to the Hall–Petch effect and the Orowan mechanism. The random orientation of the fine grains resulted in improving ductility and anisotropy.

Senile osteoporotic fractures pose a major clinical challenge due to age-related immune and skeletal dysfunctions that critically impair bone healing. Advanced biomaterials capable of compensating for impaired osteogenesis and modulating immune responses are therefore urgently needed. In this study, a biodegradable Mg-1Dy alloy was prepared via low-temperature extrusion for potential orthopedic applications. The alloy exhibited an ultimate tensile strength of 275.9\(\pm 1\) MPa and controlled degradation behavior, which was supported by a Dy-enriched surface product layer. The immunomodulatory effect of this material has been studied, and the results show that within a concentration range of 8 mM, magnesium ions can inhibit the pro-inflammatory M1 phenotype in macrophages and promote their transformation into the anti-inflammatory M2 phenotype, thereby participating in the regulation of the NF-kB pathway. In the mouse model of senile osteoporotic fractures, compared with the control group, the Mg-1Dy alloy enhanced the mineralization and bone integration of osteocytes. These results suggest that the Mg-1Dy alloy can modulate the immune microenvironment to support bone repair, underscoring its potential value in treating fractures in elderly patients with osteoporosis.

Magnesium (Mg) alloys face a critical challenge in balancing performance optimization and unintended density increases caused by high-density secondary phases. To address this, machine learning was employed to predict the density and volume of Mg-containing binary phases, aiming to guide lightweight alloy design. Using 211 experimentally observed data points, five machine learning (ML) algorithms—Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Bayesian Ridge (Bayes)—were trained and tested. Quantitative results showed that RF achieved exceptional performance in volume prediction, with a testing coefficient of determination (R²) exceeding 0.96 and a mean absolute error (MAE) of 41.0 Å3, while SVM outperformed others in density prediction with a testing R2 of 0.885 and MAE of 0.421 g/cm3. Feature importance analysis revealed that atomic count is the primary determinant of phase volume, whereas density prediction depends on the synergistic interaction of relative atomic mass and stoichiometric ratio, as further validated by SHapley Additive exPlanations (SHAP) analysis. This work establishes a physics-informed predictive model that accelerates the development of lightweight Mg alloys by mitigating high-density secondary phases, and can be extended to other alloy systems.