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In a deep-learning-based algorithm, generative adversarial networks can generate images similar to an input. Using this algorithm, an artificial three-dimensional (3D) microstructure can be reproduced from two-dimensional images. Although the generated 3D microstructure has a similar appearance, its reproducibility should be examined for practical applications. This study used an automated serial sectioning technique to compare the 3D microstructures of two dual-phase steels generated from three orthogonal surface images with their corresponding observed 3D microstructures. The mechanical behaviors were examined using the finite element analysis method for the representative volume element, in which finite element models of microstructures were directly constructed from the 3D voxel data using a voxel coarsening approach. The macroscopic material responses of the generated microstructures captured the anisotropy caused by the microscopic morphology. However, these responses did not quantitatively align with those of the observed microstructures owing to inaccuracies in reproducing the volume fraction of the ferrite/martensite phase. Additionally, the generation algorithm struggled to replicate the microscopic morphology, particularly in cases with a low volume fraction of the martensite phase where the martensite connectivity was not discernible from the input images. The results demonstrate the limitations of the generation algorithm and the necessity for 3D observations.

We performed a machine learning-aided analysis of the rolling and recrystallization textures in pure iron with different cold reduction ratios and cold-rolling directions. Five types of specimens with different cold reduction ratios and cold-rolling directions were prepared. The effect of two-way cold-rolling on the rolling texture was small at cold reduction ratios different from 60%. The cold reduction ratio in each stage hardly affected the texture evolution during cold-rolling and subsequent short-term annealing. In the case of long-term annealing, although abnormal grain growth occurred, the crystal orientation of the grains varied. Moreover, the direction of cold-rolling in each stage also hardly affected the texture evolution during cold-rolling and subsequent short-term annealing. During long-term annealing, sheets with the same cold-rolling direction in the as-received state and in the first stage showed the texture evolution of conventional one-way cold-rolled pure iron. Additionally, we conducted a machine learning-aided analysis of rolling and recrystallization textures. Using cold-rolling and annealing conditions as the input data and the degree of Goss orientation development as the output data, we constructed high-accuracy regression models using artificial neural networks and XGBoost. We also revealed that the annealing temperature is the dominant factor in the nucleation of Goss grains.

An image regression analysis using a convolutional neural network (CNN) was conducted to correlate microstructural images with the mechanical properties of tempered martensitic steel. The proposed model could link the microstructure with a property without any manual feature extraction. To demonstrate the mechanism of the CNN−based image regression algorithm, its convoluted output was dimensionally reduced using uniform manifold approximation and projection (UMAP) and then plotted.

Sub-micro dislocation cellular structures formed during rapid solidification break the strength–ductility trade-off in laser powder bed fusion (LPBF)-processed 316L stainless steel through high-density dislocations and segregated elements or precipitates at the cellular boundaries. The high-density dislocation entangled at the cellular boundary accommodates solidification strains among the cellular structures and cooling stresses through elastoplastic deformation. Columnar grains with cellular structures typically form along the direction of thermal flux. However, the ultra-low misorientations between the adjacent cellular structures and their interactions with the cellular boundary formation remain unclear. In this study, we revealed the ultra-low misorientations between the cellular structures in LPBF-processed 316L stainless steel using conventional electron backscatter diffraction (EBSD), transmission Kikuchi diffraction (TKD), and transmission electron microscopy (TEM). The conventional EBSD and TKD analysis results could provide misorientation angles smaller than 2°, while the resolution mainly depends on the specimen quality and scanning step size, and so on. A TEM technique with higher spatial resolution provides accurate information between adjacent dislocation cells with misorientation angles smaller than 1°. This study presents evidence that the TEM method is the better and more precise analytical method for the misorientation measurement of the cellular structures and provides insights into measuring the small misorientation angles between adjacent dislocation cells and nanograins in nanostructured metals and alloys with ultrafine-grained microstructures.

Abstract Autologous hematopoietic stem cell transplantation (SCT) is not a standard treatment option for Philadelphia chromosome‐positive acute lymphoblastic leukemia (Ph+ALL); however, its position has been reassessed since the introduction of tyrosine kinase inhibitors (TKIs). We prospectively analyzed the efficacy and safety of autologous peripheral blood SCT (auto‐PBSCT) for Ph+ALL patients aged between 55 and 70 years who had achieved complete molecular remission. Melphalan, cyclophosphamide, etoposide, and dexamethasone were used for conditioning. A total of 12 courses of maintenance therapy, including dasatinib, were performed. The required number of CD34+ cells was harvested in all five patients. No patient died within 100 days after auto‐PBSCT, and no unexpected serious adverse events were observed. Although 1‐year event‐free survival was 100%, hematological relapse was observed in three patients at a median of 801 days (range, 389–1088 days) after auto‐PBSCT. Molecular progressive disease was observed in the other two patients, although they maintained their first hematological remission at the last visit. Auto‐PBSCT can be safely performed for Ph+ALL with TKIs. A limitation of auto‐PBSCT was suggested, despite the increase in the intensity of a single treatment. The development of long‐term therapeutic strategies by including new molecular targeted drugs is warranted to maintain long‐term molecular remission.

We investigated the ferrite recrystallization behavior of pure iron with different dislocation characters. Two types of specimens with almost the same dislocation densities and different dislocation characters were prepared. The edge dislocation density in specimen A was larger than that in specimen B, whereas the screw dislocation density in specimen B was larger than that in specimen A. Ferrite recrystallization progressed faster in specimen A than in specimen B. Furthermore, cellular automaton simulation proved that changes in ferrite recrystallization behavior due to the dislocation character are attributed to the difference in the driving force for recrystallization and the activation energy for recovery in each specimen. Thus, we found that the difference in the dislocation character can affect the ferrite recrystallization behavior of pure iron.

Dual-phase (DP) steel has been widely used in automotive steel plates with a balance of excellent strength and ductility. Grain refinement in DP steel is important to improve the properties further; however, the factors affecting grain growth need to be well understood. The remaining problem is that acquiring data through experiments is still time-consuming and difficult to evaluate quantitatively. With the development of materials informatics in recent years, material development time and costs are expected to be significantly reduced through experimentation, simulation, and machine learning. In this study, grain growth behavior in DP steel was studied using two-dimensional (2D) and three-dimensional (3D) Monte Carlo modeling and simulation to estimate the effect of some key parameters. Grain growth can be suppressed when the grain boundary energy is greater than the phase boundary energy. When the volume fractions of the matrix and the second phase were equal, the suppression of grain growth became obvious. The long-distance diffuse frequency can promote grain growth significantly. The simulation results allow us to better understand the factors affecting grain growth behavior in DP steel. Machine learning was performed to conduct a sensitivity analysis of the affecting parameters and estimate the magnitude of each parameter’s effects on grain growth in the model. Combining MC simulation and machine learning will provide one promising research strategy to gain deeper insights into grain growth behaviors in metallic materials and accelerate the research process.

The abnormal grain growth of steel, which is occurs during carburization, adversely affects properties such as heat treatment deformation and fatigue strength. This study aimed to control abnormal grain growth by controlling the materials and processes. Thus, it was necessary to investigate the effects of microstructure, precipitation, and heat treatment conditions on abnormal grain growth. We simulated abnormal grain growth using the cellular automaton (CA) method. The simulations focused on the grain boundary anisotropy and dispersion of precipitates. We considered the effect of grain boundary misorientation on boundary energy and mobility. The dispersion state of the precipitates and its pinning effect were considered, and grain growth simulations were performed. The results showed that the CA simulation reproduced abnormal grain growth by emphasizing the grain boundary mobility and the influence of the dispersion state of the precipitate on the occurrence of abnormal grain growth. The study findings show that the CA method is a potential technique for the prediction of abnormal grain growth.

Austenite–ferrite phase transformation is a crucial metallurgical tool to tailor the properties of steels required for particular applications. Extensive simulation and modeling studies have been conducted to evaluate the phase transformation behaviors; however, some fundamental physical parameters still need to be optimized for better understanding. In this study, the austenite–ferrite phase transformation was evaluated in carbon steels with three carbon concentrations during isothermal annealing at various temperatures using a developed cellular automaton simulation model combined with Bayesian optimization. The simulation results show that the incubation period for nucleation is an essential factor that needs to be considered during austenite–ferrite phase transformation simulation. The incubation period constant is mainly affected by carbon concentration and the optimized values have been obtained as 10−24, 10−19, and 10−21 corresponding to carbon concentrations of 0.2 wt%, 0.35 wt%, and 0.5 wt%, respectively. The average ferrite grain size after phase transformation completion could decrease with the decreasing initial austenite grain size. Some other parameters were also analyzed in detail. The developed cellular automaton simulation model combined with Bayesian optimization in this study could conduct an in-depth exploration of critical and optimal parameters and provide deeper insights into understanding the fundamental physical characteristics during austenite–ferrite phase transformation.

A modified SliceGAN architecture was proposed to generate a high-quality synthetic three-dimensional (3D) microstructure image of TYPE 316L material manufactured through additive methods. The quality of the resulting 3D image was evaluated using an auto-correlation function, and it was discovered that maintaining a high resolution while doubling the training image size was crucial in creating a more realistic synthetic 3D image. To meet this requirement, modified 3D image generator and critic architecture was developed within the SliceGAN framework.

Abstract In this report, we propose a deep learning technique for high-accuracy estimation of the intensity class of a typhoon from a single satellite image, by incorporating meteorological domain knowledge. By using the Visual Geometric Group’s model, VGG-16, with images preprocessed with fisheye distortion, which enhances a typhoon’s eye, eyewall, and cloud distribution, we achieved much higher classification accuracy than that of a previous study, even with sequential-split validation. Through comparison of t-distributed stochastic neighbor embedding (t-SNE) plots for the feature maps of VGG with the original satellite images, we also verified that the fisheye preprocessing facilitated cluster formation, suggesting that our model could successfully extract image features related to the typhoon intensity class. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to highlight the eye and the cloud distributions surrounding the eye, which are important regions for intensity classification; the results suggest that our model qualitatively gained a viewpoint similar to that of domain experts. A series of analyses revealed that the data-driven approach using only deep learning has limitations, and the integration of domain knowledge could bring new breakthroughs.

An artificial T cell adaptor molecule (ATAM) was generated to improve persistence of T cell receptor (TCR) gene-transduced T (TCR-T) cells compared to such persistence in a preceding study. ATAMs are gene-modified CD3ζ with the intracellular domain of 4-1BB inserted in the middle of CD3ζ. NY-ESO-1 TCR-T cells transduced with an ATAM with two separated virus vectors demonstrated superior proliferation upon antigen stimulation. To further develop clinically applicable ATAM-transduced TCR-T cells, we attempted to make a single virus vector to transduce the TCR and ATAM simultaneously. Because we failed to observe improved proliferation capacity upon stimulation after one virus vector (1vv) transduction, we compared TCR-T cells transduced with 1vv and two virus vector (2vv) methods to elucidate the reason. In Jurkat reporter cells, an ATAM transduced by the 2vv method demonstrated a higher intensity than by the 1vv method, and the ATAM intensity was associated with increased nuclear factor κB (NF-κB) signals upon stimulation. In ATAM-transduced primary T cells, a transduced ATAM by the 2vv method showed higher intensity and better proliferation. ATAM-transduced TCR-T cells demonstrated improved proliferation only when the ATAM was transduced at a higher intensity. To create a simpler transduction method, we need to develop a strategy to make a higher ATAM expression to prove the efficacy of ATAM transduction in TCR-T therapy.

The tensile properties of ferrite single-phase low-carbon steel with different initial microstructures were evaluated. Three types of hot-rolled sheet specimens with different microstructures—specimen P (consisting of ferrite and pearlite), specimen B (consisting of bainitic structures), and specimen M (consisting of fully martensitic structures) were used. After hot rolling, these specimens were cold-rolled, subsequently heated to the finishing temperature of ferrite recrystallization, and then water-quenched to room temperature. The recrystallized ferrite grain size decreased in the specimen order of B > P > M. The distribution of cementite was comparatively homogeneous in specimens B and M, whereas that in specimen P was heterogeneous. The yield and tensile strengths decreased in the specimen order of M > P > B. Calculations using the Hall–Petch equation revealed that the yield strength of each specimen depended mainly on the recrystallized ferrite grain size. The total elongation decreased in the specimen order of B > P > M, whereas the local elongation was approximately the same in all of the specimens. In addition, the number of dimples decreased in the specimen order of M > B > P, whereas the size of dimples decreased in the specimen order of P > B > M. These results suggest that the homogeneous distribution of cementite and the fine recrystallized ferrite grains in specimen M suppress void coalescence, thereby resulting in a good balance between the tensile strength and the local elongation.

The influence of cold-rolling directions on the recrystallization texture evolution of pure iron was examined. As-received pure iron sheets were cold-rolled under two different conditions (specimens A and B). Specimen A was cold-rolled in the vertical direction against the cold-rolling direction of the as-received sheet. Specimen B was cold-rolled in the vertical direction against the cold-rolling direction of the as-received sheet, and then in the cold-rolling direction of the as-received sheet. Cold-rolled specimens were heated to each desired temperature before being quenched in water to room temperature (298 ± 2 K). Both cold-rolled specimens showed the development of γ-fiber and {100} orientation. Additionally, γ-fiber formed comparatively more in cold-rolled specimen A, while α-fiber developed comparatively more in cold-rolled specimen B. Strain distribution in cold-rolled specimen A was presumably inhomogeneous, whereas that in cold-rolled specimen B was rather uniform at the macro-scale. The formation of γ-fiber was confirmed in annealed specimen A. In annealed specimen B, however, the recrystallization texture tended to be random, and the formation of α-fiber was observed. Furthermore, the formation of Goss orientation in both annealed specimens was established. Recrystallized ferrite grains with Goss orientation nucleated in high strain regions of cold-rolled specimen. These findings show that by devising the cold-rolling direction, it is possible to discover new types of recrystallization textures.

We performed a three-dimensional (3D) analysis of ferrite grains recrystallized in low-carbon steel during annealing. Cold-rolled specimens were heated to 723 K and held for various periods. The 3D morphology of ferrite grains recrystallized during the annealing process was investigated. The progress of recovery in low-carbon steel was more inhibited than that in pure iron. However, ferrite recrystallization in low-carbon steel was more rapid than that in pure iron. The Avrami exponent was inconsistent with the 3D morphology of the recrystallized ferrite grains in pure iron but consistent with that of the grains in low-carbon steel. Thus, the Avrami exponent depends on the recovery and recrystallization behaviors. Furthermore, the recrystallized ferrite grain growth was virtually 2D. Three types of recrystallized ferrite grains were observed: recrystallized ferrite grains elongated along the transverse or rolling direction; plate-shaped recrystallized ferrite grains grown in the transverse and rolling directions; fine and equiaxed recrystallized ferrite grains. These results suggest that the recrystallized ferrite grains did not grow in the normal direction. Thus, we concluded that the 3D morphology of recrystallized ferrite grains depends on the kinetics of recrystallization and the initial microstructure before recrystallization.

We undertook a three-dimensional analysis of the recrystallization that occurs in pure iron from the viewpoint of consistency between the growth direction of the recrystallized ferrite grains and the Johnson-Mehl-Avrami-Kolmogorov (JMAK) theory. Cold-rolled specimens were heated to 550 °C and, then, held at that temperature for a range of durations. The recrystallization behavior during the annealing was investigated both theoretically and experimentally. The formation of subgrains in non-recrystallized ferrite grains was found to occur before the start of recrystallization. Additionally, the fraction that recrystallized increased with the duration at 550 °C, and the slope became gradual after the annealing duration exceeded 60 s. The values of the Avrami exponent in the early and later stages of annealing were 1.86 and 0.37, respectively. Based on the JMAK theory, the growth direction of the recrystallized ferrite grains was assumed to be one-dimensional. On the other hand, the two- and/or three-dimensional growth of the recrystallized ferrite grains was confirmed by the three-dimensional analysis. Using a modified JMAK equation that takes the effect of recovery in recrystallization during annealing into consideration, the experimental data was found to be in good agreement with the modified JMAK plot. Thus, the inconsistency between the theoretical and experimental results could be attributed to the interaction between the recovery and recrystallization.

We conducted quantitative analysis of the recovery process during pure iron annealing using the modified Williamson-Hall and Warren-Averbach methods. We prepared four types of specimens with different dislocation substructures. By increasing the annealing temperature, we confirmed a decrease in dislocation density. In particular, screw-dislocation density substantially decreased in the early stage of the recovery process, while edge-dislocation density gradually decreased as annealing temperature increased. Moreover, changes in hardness during the recovery process mainly depended on edge-dislocation density. Increases in annealing temperature weakly affected the dislocation arrangement parameter and crystallite size. Recovery-process modeling demonstrated that the decrease in screw-dislocation density during the recovery process was mainly dominated by glide and/or cross-slip with dislocation core diffusion. In contrast, the decrease in edge-dislocation density during the recovery process was governed by a climbing motion with both dislocation core diffusion and lattice self-diffusion. From the above results, we succeeded in quantitatively distinguishing between edge- and screw-dislocation density during the recovery process, which are difficult to distinguish using transmission electron microscope and electron backscatter diffraction.

Strength and work-hardening in steels are discussed from the viewpoint of heterogeneous deformation. In-situ neutron diffraction studies made it clear that misfit strains between grains accompanied grain-scaled internal stresses (intergranular stress). In a dual phase steel, the intergranular stress was superposed on the phase stress. These results show good agreement with the predictions of a simple dual-phase material model: the strong martensite phase yields higher stress than the macro-yield stress, resulting in high strengthening of (ferrite + martensite) dual phase steels. Both long-range internal stress and short-range ones such as forest dislocation hardening may cause resistance to dislocation motion in the steels. Therefore, work-hardening takes place more effectively with higher internal stress and larger volume fraction.