In this work, magnesium-based composites were obtained by shock-wave compaction of a powder mixture of Mg-5 wt.% AlN at a shock-wave pressure of 2 GPa. Their microstructure was investigated and the phase composition was determined, from which it follows that the nanoparticles retain their phase composition and are uniformly distributed in the magnesium matrix. The materials obtained by shock-wave compaction were used as master alloys for the production of magnesium alloys by die casting. The amount of aluminum nitride nanoparticles in the AZ91 magnesium alloy was 0.5 wt.%. Studies of the microstructure of the magnesium alloys showed a decrease in the average grain size of the magnesium matrix from 610 to 420 μm. Studies of mechanical properties have shown that the introduction of aluminum nitride nanoparticles increases the yield strength from 55 to 119 MPa, the tensile strength from 122 to 171 MPa and the plasticity from 4 to 6.5%, respectively. The effect of nanoparticles on the fracture behavior of the magnesium alloy under tension was determined.

Analysis of scanning electron microscope (SEM) images is crucial for characterising aluminide diffusion coatings deposited via the slurry route on steels, yet challenging due to various factors like imaging artefacts, noise, and overlapping features such as resin, precipitates, cracks, and pores. This study focuses on determining the thicknesses of the coating layers Fe₂Al₅ and, if present, FeAl, pore characteristics, and chromium precipitate fractions after the heat treatment that forms the diffusion coating. A deep learning SEM image segmentation model utilising U-Net architecture is proposed. Ground truth data were generated using the trainable Weka segmentation plugin in ImageJ, manually refined for accuracy, and supplemented with synthetic data from Blender 3D software for data augmentation of a limited number of SEM label images. The deep learning model trained on a combination of synthetic and real SEM data achieved mean dice scores of 98.7% ± 0.2 for the Fe₂Al₅ layer, 82.6% ± 8.1 for pores, and 81.48% ± 3.6 for precipitates when evaluated on manually labelled SEM data. The deep learning procedure was applied to evaluate a series of SEM images of diffusion coatings obtained with three different slurry compositions. The evaluation revealed that using a slurry without a rheology modifier may lead to a thicker partial Fe₂Al₅ layer that is formed by inward diffusion. The relation between the outward and inward diffusion Fe₂Al₅ layers was not affected by the coating thickness. The thinner diffusion coating presents lower pores and chromium precipitate fractions independently of the slurry selected.