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Adding alloying elements has long been known to improve the thermal stability of nanostructured metals via atoms segregation or/and precipitated particles. Despite this, questions regarding the effect of particles on microstructure and their synergic impacts on structural coarsening remain nebulous. Here, we demonstrate the interactions between nanoparticles and microstructural coarsening by Y-junction motion during recovery of lamellar-nanostructured Al–1.0%Si that exhibit good thermal stability. In-situ annealing experiments in a transmission electron microscope reveal three ways for particles to pin moving Y-junctions: (1) direct pinning from particles precipitated at lamellar boundaries or/and inside lamella; (2) synergic pinning from a neighbouring interconnecting dislocation boundary and the particles located at the junction of interconnecting and lamellar boundaries; (3) indirect pinning caused by the particles located at interconnecting dislocation boundaries inside neighbouring lamellae. These pinning mechanisms originating from Si nanoparticles enable high thermal stability in the lamellarnanostructured Al–1.0%Si alloy. The findings enrich the current understanding on the specific role of particles related to structural coarsening resistance in lamellar-nanostructured Al alloys.

In this work, it is investigated how grain boundaries influence the local strain determination by the microstructural feature tracking (MFT) algorithm. In this method, tetrahedra are used as the strain calculation unit. We apply the MFT processing procedure on data obtained by a crystal plasticity finite element modeling (CPFEM) simulation to explore the uncertainties in the calculated strains caused by grain boundaries. Effects of tetrahedron types and radius ratios are discussed.

Nucleation of recrystallization at large second phase particles during annealing is essential in many industrially relevant aluminum alloys. To adequately quantify the spatial distribution of such particles and their impact on nucleation in bulk materials, 3D investigations are needed. This study presents a multimodal characterization method to investigate particle stimulated nucleation in a heavily deformed AA5182 alloy. To map the spatial distribution of large second phase particles, conventional laboratory absorption contrast X-ray tomography is used, while the recrystallization nuclei are revealed by Laue micro-diffraction using synchrotron X-rays. The registration of data obtained by the two different non-destructive high-resolution experimental techniques is described. Finally, some preliminary results are presented.

Laboratory X-ray diffraction contrast tomography (LabDCT) is a novel imaging technique for non-destructive 3D characterization of grain structures. An accurate grain reconstruction critically relies on precise segmentation of diffraction spots in the LabDCT images. The conventional method utilizing various filters generally satisfies segmentation of sharp spots in the images, thereby serving as a standard routine, but it also very often leads to over or under segmentation of spots, especially those with low signal-to-noise ratios and/or small sizes. The standard routine also requires a fine tuning of the filtering parameters. To overcome these challenges, a deep learning neural network is presented to efficiently and accurately clean the background noise, thereby easing the spot segmentation. The deep learning network is first trained with input images, synthesized using a forward simulation model for LabDCT in combination with a generic approach to extract features of experimental backgrounds. Then, the network is applied to remove the background noise from experimental images measured under different geometrical conditions for different samples. Comparisons of both processed images and grain reconstructions show that the deep learning method outperforms the standard routine, demonstrating significantly better grain mapping.

Background: The internal strain distribution developing during plastic deformation is important for understanding the mechanical properties of polycrystalline materials. Such distributions may be determined by the microstructural feature tracking method. However, the strain accuracy and the sources of uncertainty of this method are not well quantified yet. Objective: Evaluate the accuracy of local strain determination based on laboratory-based multimodal X-ray tomography measurements. Methods: The plastic deformation behavior of a particle-contained fully recrystallized Al-4mass%Cu alloy was characterized during in-situ tensile testing using absorption contrast tomography to reveal marker particles and diffraction contrast tomography to reveal the grain structure. Results: It was found that the accuracy in strain measurement is inversely proportional to the particle distance, whereas it is independent of the particle size, and the accuracy is not biased by grain boundaries. As an illustrative example, the preliminary analysis of the microstructure-strain relationships reveals significant strain differences both between and within individual grains. Conclusions: The results document the validity and limitations of the microstructural feature tracking method for local strain measurements using a laboratory X-ray source, are of importance for local strain analysis in general and may be considered as an alternative to other methods, e.g. digital image correlation. The measurements allow evaluation of effects of the grain microstructure on the local strain development during plastic deformation. By a preliminary analysis of the strain difference between two selected grains, it is suggested that the grain shape and crystallographic relationships between neighboring grains are of importance for the development of local strains.

The migration of recrystallization boundaries into spatially varying deformation energy fields are simulated based on a two-dimensional phase-field model. The energy fields are chosen as idealized versions of deformation microstructures subdivided by two sets of intersecting geometrically necessary dislocation boundaries (GNBs), and effects of the sharpness of the GNBs are investigated. The simulations show that the shape of the recrystallizing grain as well as the recrystallization kinetics are considerably affected by the wall characteristics of the GNBs. Recrystallization occurs faster in the deformed matrix with “sharper” walls. The simulation results highlight the importance of the deformation microstructure characteristics on the recrystallization kinetics, and as the deformation microstructures depend on the initial grain orientations, the results also indicate tight relations between initial, deformation and recrystallization textures