The influence of the volume fraction of the long-period stacking ordered (LPSO) phase on the strengthening mechanisms acting in Mg/LPSO two-phase extruded alloys is discussed by focusing on compression tests of Mg₉₄Zn₂Y₄ and Mg₉₂Zn₃Y₅ alloys. An increase in the LPSO phase volume fraction increases the yield stress of these alloys, but the magnitude of the increase is not monotonic with the volume fraction. For deformation parallel to the extrusion direction, the rate of increase in the yield stress shows two large gaps between the Mg_99.2Zn_0.2Y_0.6/Mg₉₇Zn₁Y₂ and Mg₉₂Zn₃Y₅/Mg₈₉Zn₄Y₇ alloys. This is derived from the change in strengthening mechanisms. The upper gap between Mg₉₂Zn₃Y₅/Mg₈₉Zn₄Y₇ is derived from the change in the strengthening mechanism between short-fiber reinforcement and the simple rule of mixtures. The lower gap between Mg_99.2Zn_0.2Y_0.6/Mg₉₇Zn₁Y₂ corresponds to the existence of a short-fiber strengthening mechanism or not. As the volume fraction of the LPSO phase decreases, the magnitude of kink-band strengthening to the yield stress decreases; however, it is still effective even in alloys with a low volume fraction of the LPSO phase.

We first fabricated the single crystal of 316L stainless steel by laser powder bed fusion (L-PBF), focusing on the applicability of the µ-Helix scanning strategy with narrow pitch as a method for obtaining a single crystal. Various combinations of laser power and laser scanning speed were examined. The cubic block samples were orientated to 〈100〉 in the X-laser scanning direction, to 〈110〉 in the Y-laser scanning direction, and to 〈110〉 in the building direction, which is contrary to that obtained by the µ-Helix scanning strategy in electron beam melting (EBM), in which the X- and Y-laser scanning directions are orientated to 〈110〉, and Z-direction is oriented to 〈100〉 direction. Also, it has been demonstrated that melt pool monitoring by on-axis and off-axis dual photodiodes can detect the nonequivalence of ±X-scanning and ±Y-scanning, which is responsible for the unexpected crystal orientation.

Metal additive manufacturing enables producing complex geometric structures with high accuracy and breaks the design constraints of traditional manufacturing methods. Laser powder bed fusion, a typical additive manufacturing process, presents a challenge in experimentally understanding the nano-scaled microstructure-process relationship regarding the wide range of process parameters. In this study, we aim to reveal the novel nanoscale structural features by advanced scanning transmission electron microscopy to clarify the formation mechanisms in 316L stainless steel by laser powder bed fusion. Here we show that the slender columnar grains were confined to the centreline of the melt pool along the build direction, and the columnar cell structure at the side branching of the melt pool grew along orthogonal directions to follow drastic changes in thermal gradient across adjacent melt pools. Novel nano-scaled modulated structures have been observed in the dislocation cells parallel to the laser scan direction, which were mainly caused by the elastic strain involving the thermal gradient inside the melt pool and across adjacent melt pools as well as the effective strain field in the dislocation cell interiors. An in-depth understanding of microstructure developments is worthy of fabricating high-performance materials by controlling the additive manufacturing process.

In this work, we constructed machine learning models to predict structural descriptors that numerically represent the atomic structures in three dimensions from x-ray absorption near-edge structure (XANES) spectra. The neural network models that predict radial distribution functions (RDF) and orbital-field matrix (OFM), a descriptor that deals with the anisotropy of the local structure, the valence electron number of the ligand, and orbital information, were constructed. We used more than 120,000 O K-edge XAS spectra data from the Materials Project database as the training data set. We successfully constructed models that roughly predicted RDFs with 74% of the test data. Furthermore, the model that predicted OFM also captured an overview of OFM in 97% of the test data. These results demonstrate that the atomic structural information can be directly extracted from XANES spectra using neural network models.

Atomic stress, utilized in molecular mechanics and molecular dynamics, is valuable in analyzing complex phenomena such as heat transfer, crack propagation and void growth. However, traditional modeling techniques designed for large-scale systems may lack the precision achievable through first-principles calculations. To overcome this limitation, we propose an approach based on artificial neural network (ANN) potentials to compute atomic stress. A crucial aspect of this method is the use of central force decomposition to derive the atomic stress tensor of the ANN potential, ensuring compliance with the balance between linear and angular momentum. By comparing atomic stress calculations for surface systems in Fe and Al using the ANN and embedded-atom (EAM) potentials, we demonstrate that the ANN potential accurately reproduces the stress oscillations near the surface layer predicted by first-principles calculations. This scheme allows us to evaluate atomic stress with nearly the same accuracy as first-principles calculations, even in large-scale models with complex geometries and defect structures.