It is know from literature that small additions(<1 wt%)of Ca,Al and Zn significantly improve the intrinsic ductility of Mg.The exact role of each element,both qualitatively and quantitatively,and their combined eff...It is know from literature that small additions(<1 wt%)of Ca,Al and Zn significantly improve the intrinsic ductility of Mg.The exact role of each element,both qualitatively and quantitatively,and their combined effects,however,are poorly understood.Here we achieved a much clearer view on the quantitative role of each element with respect to ductility improvement and on the collaborative effect,particularly of Ca and Zn in Mg.Some of our findings and conclusions are in disagreement with data and interpretation found in literature.Four different alloys,namely,Mg-0.1 Ca,Mg-0.1 Ca-1 Al,Mg-0.05 Ca-1 Al,Mg-0.1 Ca-2 Al-1 Zn(all are in wt%)were selected for this investigation.All alloys were treated such that approx.similar grain sizes and textures were obtained.This largely excludes the effect of extrinsic factors on ductility.EBSD-guided slip trace analyses reveal that the addition of Ca eases activation of prismatic and pyramidal II slip systems.Using in-situ deformation experiments in SEM and atom probe tomography observations of grain boundaries direct evidence is given for the individual and synergetic effects of Ca and Zn on grain boundary cohesion as an important contribution to improve the ductility of these alloys.We conclude that Ca reduces the slip anisotropy and ameliorates ductility,however,the weak grain boundary cohesion in the Mg-0.1 wt%Ca alloy limits the material’s tensile ductility.The addition of Zn alters the Ca segregation at the grain boundaries and helps to retain their cohesive strength,an effect which thus enables higher ductility and strength.The further addition of Al primarily improves the strength.The results show that the balanced influence of reduced slip anisotropy on the one hand and increased grain boundary cohesion on the other hand allow to design a high strength high ductility rare-earth free Mg alloy.展开更多
Nanoscale L12-type ordered structures are widely used in face-centered cubic(FCC)alloys to exploit their hardening capacity and thereby improve mechanical properties.These fine-scale particles are typically fully cohe...Nanoscale L12-type ordered structures are widely used in face-centered cubic(FCC)alloys to exploit their hardening capacity and thereby improve mechanical properties.These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species,which makes them challenging to be characterized.Spatial distribution maps(SDMs)are used to probe local order by interrogating the three-dimensional(3D)distribution of atoms within reconstructed atom probe tomography(APT)data.However,it is almost impossible to manually analyze the complete point cloud(>10 million)in search for the partial crystallographic information retained within the data.Here,we proposed an intelligent L1_(2)-ordered structure recognition method based on convolutional neural networks(CNNs).The SDMs of a simulated L1_(2)-ordered structure and the FCC matrix were firstly generated.These simulated images combined with a small amount of experimental data were used to train a CNN-based L1_(2)-ordered structure recognition model.Finally,the approach was successfully applied to reveal the 3D distribution of L1_(2)–typeδ′–Al3(LiMg)nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system.The minimum radius of detectable nanodomain is even down to 5Å.The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.展开更多
基金the financial support by the international doctoral school IMPRS,Surmat。
文摘It is know from literature that small additions(<1 wt%)of Ca,Al and Zn significantly improve the intrinsic ductility of Mg.The exact role of each element,both qualitatively and quantitatively,and their combined effects,however,are poorly understood.Here we achieved a much clearer view on the quantitative role of each element with respect to ductility improvement and on the collaborative effect,particularly of Ca and Zn in Mg.Some of our findings and conclusions are in disagreement with data and interpretation found in literature.Four different alloys,namely,Mg-0.1 Ca,Mg-0.1 Ca-1 Al,Mg-0.05 Ca-1 Al,Mg-0.1 Ca-2 Al-1 Zn(all are in wt%)were selected for this investigation.All alloys were treated such that approx.similar grain sizes and textures were obtained.This largely excludes the effect of extrinsic factors on ductility.EBSD-guided slip trace analyses reveal that the addition of Ca eases activation of prismatic and pyramidal II slip systems.Using in-situ deformation experiments in SEM and atom probe tomography observations of grain boundaries direct evidence is given for the individual and synergetic effects of Ca and Zn on grain boundary cohesion as an important contribution to improve the ductility of these alloys.We conclude that Ca reduces the slip anisotropy and ameliorates ductility,however,the weak grain boundary cohesion in the Mg-0.1 wt%Ca alloy limits the material’s tensile ductility.The addition of Zn alters the Ca segregation at the grain boundaries and helps to retain their cohesive strength,an effect which thus enables higher ductility and strength.The further addition of Al primarily improves the strength.The results show that the balanced influence of reduced slip anisotropy on the one hand and increased grain boundary cohesion on the other hand allow to design a high strength high ductility rare-earth free Mg alloy.
基金supported by the National Natural Science Foundation of China(51971247)the open Foundation of State Key Laboratory of Powder Metallurgy at Central South University,Changsha,China。
文摘Nanoscale L12-type ordered structures are widely used in face-centered cubic(FCC)alloys to exploit their hardening capacity and thereby improve mechanical properties.These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species,which makes them challenging to be characterized.Spatial distribution maps(SDMs)are used to probe local order by interrogating the three-dimensional(3D)distribution of atoms within reconstructed atom probe tomography(APT)data.However,it is almost impossible to manually analyze the complete point cloud(>10 million)in search for the partial crystallographic information retained within the data.Here,we proposed an intelligent L1_(2)-ordered structure recognition method based on convolutional neural networks(CNNs).The SDMs of a simulated L1_(2)-ordered structure and the FCC matrix were firstly generated.These simulated images combined with a small amount of experimental data were used to train a CNN-based L1_(2)-ordered structure recognition model.Finally,the approach was successfully applied to reveal the 3D distribution of L1_(2)–typeδ′–Al3(LiMg)nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system.The minimum radius of detectable nanodomain is even down to 5Å.The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.
