Influence of Composition on the Evolution of Dislocation Substructure in the Low-Stability State Polycrystalline Cu-Al Alloys under Plastic Deformation

New concepts of dislocation physics of plasticity and strength are considered using quantitative methods of transmission diffraction electron microscopy. New concepts of dislocation physics of plasticity and strength are considered using quantitative methods of transmission diffraction electron microscopy. The analysis of changes in the parameters of the dislocation substructure (DSS) is given on the example of alloys Cu-0.5 and 14 аt. % Al and the influence of these parameters on the change in the substructure of the material at a temperature T=293 K is considered. It is shown that at each stage of deformation, there are usually two substructures ("old" and "new"). The blurring of the transition from stage to stage is associated with the presence of weakly stable pre-transition structural-phase States at certain degrees of deformation of several types of substructures simultaneously, i.e., a weakly stable structural-phase state of the system. Against the background of the "old" substructure, a "new" one is born, which in the process of deformation becomes the main one, and then the "old" one, in the depths of which another substructure is formed. Experimental evidence of this regularity is obtained for FCC alloys. The presence of grain boundaries complicates the diagrams: a third substructure is formed near the grain boundaries, which corresponds to the following substructures (later) in the sequence of DSS transformations.

Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

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 L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-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 L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type δ′–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 5 Å. 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.