The relation between ductility at high temperature and solid solution in Mg alloys

This work investigates the effect of solid solution on ductility and on the activation of individual deformation mechanisms at moderate temperatures and at quasi-static strain rates in Mg-Zn and Mg-Al alloys. With that aim, four solid solution Mg-Zn and Mg-Al binary alloy ingots containing 1 and 2 wt.% solute atoms were subjected to hot rolling and subsequent annealing to generate polycrystals with similar average grain size and basal-type texture for each composition. The activity of the different slip systems after tensile testing at 150°C and at 250°C was evaluated in pure Mg and in the alloys by EBSD-assisted slip trace analysis. In addition, segregation of Zn and Al atoms at grain boundaries during the thermo-mechanical processing was characterized by HAADF-STEM and EDX. It was found that while the addition of Al and Zn atoms to pure Mg does not lead to major changes in the mechanical strength at the investigated temperatures, it does enhance ductility significantly, especially at 250℃. Our results show that this increase in ductility cannot be attributed to a higher activation of non-basal systems in the alloys, as reported earlier, as the incidence of non-basal systems is indeed considerably higher in pure Mg. This work suggests, on the contrary, that the ductility increase may be attributed to the presence of a more homogenous basal activity in the alloys due to a lower degree of orientation clustering, to grain boundary solute segregation, and to a higher slip diffusivity at grain interiors.

Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods

The aim of this work is to predict,for the first time,the high temperature flow stress dependency with the grain size and the underlaid deformation mechanism using two machine learning models,random forest(RF)and artificial neural network(ANN).With that purpose,a ZK30 magnesium alloy was friction stir processed(FSP)using three different severe conditions to obtain fine grain microstructures(with average grain sizes between 2 and 3μm)prone to extensive superplastic response.The three friction stir processed samples clearly deformed by grain boundary sliding(GBS)deformation mechanism at high temperatures.The maximum elongations to failure,well over 400% at high strain rate of 10^(-2)s^(-1),were reached at 400℃ in the material with coarsest grain size of 2.8μm,and at 300℃ for the finest grain size of 2μm.Nevertheless,the superplastic response decreased at 350℃ and 400℃ due to thermal instabilities and grain coarsening,which makes it difficult to assess the operative deformation mechanism at such temperatures.This work highlights that the machine learning models considered,especially the ANN model with higher accuracy in predicting flow stress values,allow determining adequately the superplastic creep behavior including other possible grain size scenarios.