Effects of the extrusion parameters on microstructure,texture and room temperature mechanical properties of extruded Mg-2.49Nd-1.82Gd-0.2Zn-0.2Zr alloy

Microstructure,texture,and mechanical properties of the extruded Mg-2.49Nd-1.82Gd-0.2Zn-0.2Zr alloy were investigated at different extrusion temperatures(260 and 320℃),extrusion ratios(10:1,15:1,and 30:1),and extrusion speeds(3 and 6 mm/s).The experimental results exhibited that the grain sizes after extrusion were much finer than that of the homogenized alloy,and the second phase showed streamline distribution along the extrusion direction(ED).With extrusion temperature increased from 260 to 320℃,the microstructure,texture,and mechanical properties of alloys changed slightly.The dynamic recrystallization(DRX)degree and grain sizes enhanced as the extrusion ratio increased from 10:1 to 30:1,and the strength gradually decreased but elongation(EL)increased.With the extrusion speed increased from 3 to 6 mm/s,the grain sizes and DRX degree increased significantly,and the samples presented the typical<2111>-<1123>rare-earth(RE)textures.The alloy extruded at 260℃ with extrusion ratio of 10:1 and extrusion speed of 3 mm/s showed the tensile yield strength(TYS)of 213 MPa and EL of 30.6%.After quantitatively analyzing the contribution of strengthening mechanisms,it was found that the grain boundary strengthening and dislocation strengthening played major roles among strengthening contributions.These results provide some guidelines for enlarging the industrial application of extruded Mg-RE alloy.

Understanding the creep behaviors and mechanisms of Mg-Gd-Zn alloys via machine learning

Mg-Gd-Zn based alloys have better creep resistance than other Mg alloys and attract more attention at elevated temperatures.However,the multiple alloying elements and various heat treatment conditions,combined with complex microstructural evolution during creep tests,bring great challenges in understanding and predicting creep behaviors.In this study,we proposed to predict the creep properties and reveal the creep mechanisms of Mg-Gd-Zn based alloys by machine learning.On the one hand,the minimum creep rates were effectively predicted by using a support vector regression model.The complex and nonmonotonic effects of test temperature,test stress,alloying elements,and heat treatment conditions on the creep properties were revealed.On the other hand,the creep stress exponents and creep activation energies were calculated by machine learning to analyze the variation of creep mechanisms,based on which the constitutive equations of Mg-Gd-Zn based alloys were obtained.This study introduces an efficient method to comprehend creep behaviors through machine learning,offering valuable insights for the future design and selection of Mg alloys.