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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.

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.

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.

PURPOSEWe developed algorithms to identify patients with newly diagnosed cancer from a Japanese claims database to identify the patients with newly diagnosed cancer of the sample population, which were compared with the nationwide cancer incidence in Japan to assess the validity of the novel algorithms.METHODSWe developed two algorithms to identify patients with stomach, lung, colorectal, breast, and cervical cancers: diagnosis only (algorithm 1), and combining diagnosis, treatments, and medicines (algorithm 2). Patients with newly diagnosed cancer were identified from an anonymized commercial claims database (JMDC Claims Database) in 2017 with two inclusions/exclusion criteria: selecting all patients with cancer (extract 1) and excluding patients who had received cancer treatments in 2015 or 2016 (extract 2). We estimated the cancer incidence of the five cancer sites and compared it with the Japan National Cancer Registry incidence (calculated standardized incidence ratio with 95% CIs).RESULTSThe number of patients with newly diagnosed cancer ranged from 219 to 17,840 by the sites, algorithms, and exclusion criteria. Standardized incidence ratios were significantly higher in the JMDC Claims Database than in the national registry data for extract 1 and algorithm 1, extract 1 and algorithm 2, and extract 2 and algorithm 1. In extract 2 and algorithm 2, colorectal cancer in male and stomach, lung, and cervical cancers in females showed similar cancer incidence in the JMDC and national registry data.CONCLUSIONThe novel algorithms are effective for extracting information about patients with cancer from claims data by using the combined information on diagnosis, procedures, and medicines (algorithm 2), with 2-year cancer-treatment history as an exclusion criterion (extract 2).

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.

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.

We investigated differences in the hydrogen thermal desorption mechanism for Ni-Ti superelastic alloys following hydrogen charging by cathodic charging in a 0.9% NaCl aqueous solution and by immersion testing in a 0.2% APF aqueous solution. For the immersed specimen, the presence of corrosion products on the surface resulted in an upward shift of the hydrogen desorption peak by approximately 100 °C. When the total amount of desorbed hydrogen was almost the same for both specimens, a higher fraction was desorbed at temperatures below 200 °C for the cathodically charged specimen. Furthermore, a larger amount of hydride was formed for the cathodically charged specimen. These results indicate that the hydrogen thermal desorption mechanism depends on the presence of corrosion products on the surface and the amount of hydride formed.

A plastic balloon of 5000 m^3 was launched at Syowa Station (L=61), Antarctica, at 1138 UT on December 12, 1976, and the horizontal electric field and auroral X-rays (>25 keV) were simultaneously measured for about 10 hours m the stratosphere. During the balloon flight a weak geomagnetic substorm occurred corresponding to the eastward electrojet in the afternoon side of the auroral zone, and the electric field increased from 10 mV/m to 40 mV/m. There were good correspondences between the southward electric field and geomagnetic horizontal component and between the westward electric field and declination. Observed auroral X-rays were homogeneous throughout the all sky and of diffuse type. The X-rays were enhanced three times during the substorm. The electric field clearly decreased on the second X-rays event but there was no clear change on the other two events. There was no special change in the electric field direction during the geomagnetic substorm. One hour averages of the observed electric field show a clear S_q^p pattern. The height-integrated conductivities were calculated from the observed electric field and the geomagnetic variations at Syowa Station, getting the values of 1-10 mho. The Hall conductivity increased during the substorm.

Quiet-time DC and AC (18-220 Hz) electric fields were observed in the lower ionosphere up to 118 km by means of spherical double probes on board the sounding rocket S-210JA-29 which was launched from Syowa Station at 16.15 UT on July 12, 1977 The DC electric field intensity was～10 mV/m in the lower E region where the electron density was 2-3×10^4 cm^, while it increased sharply up to ～130 mV/m above the altitude of ～110 km with a good correlation with the decrease in the electron density. 0nly weak AC noise was observed in the whole altitude range

The measurements of vector electric fields and currents were made with two plastic balloons of 5,000 m^3 launched from Syowa Station (L=8.4), Antarctica, on 28 November and 29 December, 1972. The results give the following. (1) The vertical electric field and current at the altitude of 30km are 0.12-0.21 V/m and 1.3-2.2 x10^ A/m^2 respectively, and their diurnal variations are roughly consistent with the expected variation from the average world-wide thunderstorm activity. (2) The electrical conductivities were estimated from the ratio of the electric current to the electric field giving the average value of 1 x 10^ mho/m at the altitude of 30 km. This value is about twice as large as that in Japan. This can be explained by the latitudinal effect of the cosmic ray ionization in the atmosphere. (3) Enhancements of the electric field and current were observed in both vertical and horizontal components of the magnitude of about 80 mV/m and 0.8 x 10^ A/m^2 respectively at an initial stage of a magnetic substorm. The vertical electric field on the ground was also correspondingly enhanced by about 80 V/m at the same time. (4) Comparisons of observed horizontal electric field vectors with the magnetic fields observed at Syowa Station show that the magnetic field variations are caused by the ionospheric Hall current. (5) One hour averages of the horizontal electric field show large scale convection fields consistent with S^p_q in the polar ionosphere and with DP-2 in the equatorial magnetosphere. The average ionospheric and magnetospheric equatorial electric fields were 40 mV/m and 0.98 mV/m respectively, and corresponding plasma convection velocities were estimated to be 700 m/s and 22 km/s respectively.

DC and AC (8-110 Hz) electric fields were observed in the auroral ionosphere up to 120 km with spherical double probes on board the sounding rockets S-210JA-24 and 25 at Syowa Station. The rockets passed close to auroras. The DC electric fields observed during substorms at magnetic midnight are composed of the height-independent electric field of about 20 mV/m and the local electric field of up to 30 mV/m. The former electric field is the large scale electric field generally existing in the auroral ionosphere, and the latter local electric field will be the effect of space charges produced by precipitating auroral particles, peaking at the heights between 100-115 km. Strong AC noises were observed in the limited altitude range of 96-112 km during both the ascent and the descent. The noise frequencies varied with altitude and were ranged between 20 and higher than 110 Hz. The noises appeared in the direction perpendicular to the DC electric field. These characteristics suggest that the noises might be generated by plasma instability in the ionospheric current layer.