Materials Integration is the concept of accelerating materials development by linking processing, structure, property, and performance (PSPP) on a computer using any types of models such as theoretical, empirical, numerical-simulation, and machine learning models. In the first and second phases of Cross-ministerial Strategic Innovation Promotion Program in Cabinet Office, Japan, we have developed a system called MInt (Materials Integration for Network Technology), which links PSPP with computational workflows that combine modules implemented, in order to realize the concept of Materials Integration. MInt is equipped with an application programming interface (API) that can be called from various algorithms in the artificial intelligence (AI) field and one can use MInt-API together with the AI algorithms to inversely design materials and processes from desired performance. The target material systems have expanded to steel, aluminum alloys, nickel alloys, and titanium alloys, and the target processes have also expanded to welding, heat treatment, 3D additive manufacturing, and powder metallurgy. MInt is more than just software for materials design; it is designed to serve as a digital platform for industry-academia collaboration. The Materials Integration Consortium has been established with MInt as its core technology, based on the philosophy of sharing tools such as modules and workflows, while competing on how to use them. In materials research and development, which has traditionally been regarded as a competitive area, we hope that a digital collaborative area will be formed and that investment efficiency will be drastically improved.

Information on the electrical conductivity of high-temperature molten oxides can be used as very useful information not only for conventional metallurgical processes on the Earth, but also for the development of extraterrestrial resources such as lunar soil. The models for estimating electrical conductivity of molten slag developed so far apply only to a limited number of slag systems. In this study, a neural network modeling was established to reliably estimate the electrical conductivity of multi-component slag containing TiO₂. In neural network modeling, the electrical conductivity of multi-component slag was used as teaching data to conduct the learning process and Bayesian optimization was applied to determine the estimation precision. As a result, it was possible to construct a reliable neural network estimation model for electrical conductivity that was highly consistent with experimental values collected from the literature data, and the effect of oxide addition on electrical conductivity was investigated using the estimation model. This suggests that it is possible to build a reliable electrical conductivity estimation model by the neural network modeling of accumulated experimental data for multi-component slag, and it is expected that the model will be applied to the electrical conductivity estimation of unknown multi-component slag systems.

In the sintering process, productivity and strength are essential indicators, and it is known that they are closely related to the sintering time and temperature. Since the sintering time and high temperature holding time vary greatly depending on the combustion behavior of coke breeze, various studies have been conducted on the influence of oxygen, which is a critical element in sintering phenomena. However, the influence of oxygen enrichment on sinter strength and yield has not yet been unified. In this study, sintering productivity, strength and yield were investigated when the oxygen concentration of the inlet gas was increased to 40 vol%. In addition, the heat profile of pot tests and the effect of the oxygen partial pressure on the melting property were analyzed from the viewpoint of thermodynamics. As a result, it was found that yield showed the maximum value at 30 vol%-O₂ and strength increased with oxygen enrichment. As for yield, it was found that the effect of the heat profile was significant, and deviation between the heat transfer speed and the combustion speed started when the oxygen concentration exceeded 30 vol%. Regarding strength, it was suggested that strength improvement was caused by the increase in the amount of melt produced by oxygen enrichment in addition to the effect of the high temperature holding time.

Fine particles can reduce the blast furnace’s gas and liquid permeability. The residual fine particle sampling and screening of the overhaul blast furnace were carried out to obtain particle size distribution. Chemical analysis, XRD, and SEM-EDS were used to analyze the chemical and phase composition, degree of graphitization, and microstructure to clarify the characteristics and evolution of fine particles. The results show a clear correlation between particle size and chemical composition. Although the phase components of different-size particles are similar in the same area, their chemical contents are quite different. The chemical and phase composition of particles with the same particle size significantly differ in the other regions. The fine particles in the S1 region mainly consist of coke powder, ZnO, and alkali metals. Significant amounts of slag iron minerals and C coexist in the fine particles of the S2 area. The slag iron minerals contents and the carbon graphitization degree of fine particles increase as the furnace burden descends.

Carbonaceous materials (CMs) in blast furnace dust (BFD) contain abundant information about evolution behavior and utilization efficiency of metallurgical coke and injected pulverized coal. A novel method for determination of origin and content of BFD by micro-Raman spectroscopy was proposed in this paper. Ten randomly areas was selected and each one covers an area of 120×120 µm in the surface of briquetting demineralization-BFD sample. Average area was multi-point scanning measured by micro-Raman spectrometer for 140 test points with the same step length. Then, contrasted CM including evolutive coke, coal char and precipitated carbon from deposition of carbon process were prepared in laboratory simulated the process of fuels under gradually heating up condition of blast furnaces (BFs) and tested by micro-Raman spectroscopy. Meanwhile, the raceway coke was also sampled and tested for compared with coke at high temperature. Typical feature of spectrogram of these CMs can be well correspond to the BFD results. Therefore, CMs in BFD could be attributable to different sources, and its content can be counted by statistics of spectrogram. By comparing the identification results of petrography method, this method can measure the small particle size such as soot derived from very fine pulverized coal and volatiles which is hard to measure by the latter. For the investigated BF, unconsumed char escape from raceway and powdery coke in stack contributed the main source of CMs in BFD, while content of overflowing coke powder from high-temperature region of BF and precipitated carbon by deposition carbon reaction is very low.

In this study, water model experiments were performed using a mechanically agitated vessel, and a mathematical model was developed to investigate the effect of the size distribution of inclusions on turbulent agglomeration behavior. A 3 mol L⁻¹ KCl aqueous solution and two types of polydispersed acrylic particles with different average particle sizes were used in the water model experiments. Furthermore, a variable agglomeration coefficient was applied to the model using an approximate expression as a function of the size ratio and dimensionless values of the viscous and van der Waals forces. This agglomeration coefficient was applied to the Smoluchowski population balance equation; consequently, the resulting agglomeration curves were slower than those obtained with a constant agglomeration coefficient. This observation was in a good agreement with the experimental results, except for the case of a high agitation rate and a large particle size, possibly due to the breaking up of the aggregates. Therefore, it was concluded that the variable agglomeration coefficient is suitable for the agglomeration of polydispersed particles.

In this study, the rising and breakup of nitrogen bubbles in high nitrogen molten steel considering different solidification pressure has been numerically simulated by the VOF model verified comparing velocity and deformation of bubbles in water. Rising process of nitrogen bubble in the molten steel is divided into three stages. In stage I, nitrogen bubble experiences an inward depression and splits into a main bubble and two daughter bubbles. In stage II, the main bubble deforms slightly, and a few discrete bubbles split from both sides of the main bubble. In stage III, the main bubble rises to the surface of molten steel, and a large number of discrete bubbles split from the main bubble. As the solidification pressure increases from 0.1 to 2 MPa, the area of main and daughter bubbles decreases. Moreover, the jet becomes stronger with the solidification pressure, which makes the daughter bubbles more prone to splitting out. The total rising time and the maximum rising velocity of the main nitrogen bubble decrease with increasing solidification pressure. This decrement weakens the disturbance of the bubbles rising on the high nitrogen molten steel in stage III, leading to a decrease in the number of discrete bubbles.

The stability of crown is a crucial factor in ensuring the quality of hot-rolled strips. In this study, a hybrid model based on ensemble learning is developed, incorporating four reliable ML models, namely support vector machine (SVM), gaussian process regression (GPR), artificial neural network (ANN), and random forest (RF). To enhance the accuracy and interpretability of the resulting crown model, pretreatment methods such as feature selection and cluster analysis are employed. The feature selection method based on mechanism analysis and maximum information coefficient (MIC) is used to obtain the optimized feature subset, while the K-means clustering algorithm is utilized to measure data similarity and cluster data points with high similarity. Analysis of experimental results indicates that the four single ML models exhibit good prediction performance for strip crown, with determination coefficients above 0.96. The hybrid model outperforms each of the single models in terms of prediction accuracy. Moreover, the incorporation of pretreatment methods leads to an increase in the determination coefficient and a decrease in the root mean square error for each model, culminating in the superior overall performance of the hybrid model established after pretreatment. These findings highlight the potential of the proposed approach for improving the accuracy and reliability of ML models in complex industrial environments.

In this study, deformation behaviors at the grain level of coarse-grained ultralow carbon steel subjected to uniaxial tension and simple shear were simulated by using a crystal plasticity finite-element method. Heterogeneity of strain distributions appeared at the early stage and remained almost unchanged in the following deformation. Localized strain bands occurred at the grain level, but the directions of the bands depended on the deformation mode. These trends agreed well with experimental results reported in a previous paper [Hama et al., ISIJ Int., 61 (2021), 1971]. The mechanisms that the direction of the localized strain bands depended on the deformation mode were studied on the basis of the slip activities. The activities of slip systems roughly followed the Schmid factor, and the slip directions of the most active slip systems were consistent with the directions of localized strain bands, suggesting that the direction of localized strain bands were determined primarily by the Schmid factor.

Dissimilar welding of thin plates of conventional pure titanium and steel was investigated using an insert metal of the chromium plate to avoid the formation of brittle intermetallic compounds consisting of Ti and Fe. The Cr atom also can form intermetallic compounds with Ti atom though the formability is less than that of Ti/Fe. Therefore, the effect of dilution of Cr on the forming of intermetallic compounds in the weld metal by laser welding process, which has larger cooling rate comparing to the conventional arc welding, was evaluated. The weld metals with various level of dilution by Cr were obtained by adjusting the offset of laser beam irradiation from the interface of titanium and chromium plates. As the result it was found that the formation of intermetallic compound was prevented in the condition of the dilution lower than about 40%. However, the excess offset to reduce the dilution of chromium plate can cause weld defects such as the lack of fusion. That problem was solved by achieving low dilution with the addition of titanium wire in the laser welding process without offset. In conclusion, it was confirmed the sound dissimilar welded joints resulting in sufficient mechanical properties were obtained by applying the proper welding condition based on the findings in this work.

The transformation behavior of retained austenite in carburized SCM420H steel and its effect on the change in residual stress on a surface are investigated. The retained austenite on the carburized steel is transformed significantly in the first cycle of fatigue loading with a stress ratio of −1, and the transformation is less significant thereafter. Tensile loading significantly affects the transformation of retained austenite as compared with compressive loading. This is because the mechanical driving force that contributes to the martensitic transformation of retained austenite differs under tensile and compressive stresses. To investigate the transformation behavior of retained austenite and residual stress more comprehensively, an in situ X-ray measurement of retained austenite and residual stress is conducted. In the measurement, retained austenite or residual stress on a specimen surface is measured under loading conditions. Results show that the transformation of retained austenite under tensile loading involves a threshold stress value at which the transformation begins. The transformation of retained austenite proceeds when the applied stress exceeds the threshold value; however, no transformation occurs regardless of the number of loading cycles when the applied stress does not exceed the threshold. The compressive residual stress on the specimen surface increases as retained austenite transforms to martensite due to stress loading.

In this paper, the effect of cyclic softening properties on fatigue crack propagation behavior was investigated. Ferrite and ferrite-pearlite steels with different cyclic softening properties were produced by cold rolling process. The cold-rolled steels showed cyclic softening, and the cyclic softening rate increased as the cold reduction rate increased. As a result of fatigue crack propagation tests using CT specimens, the fatigue crack growth rate decreased with increasing the cold reduction rate. The crack growth rate and the cyclic softening rate showed a good correlation regardless of the microstructure. The cold-rolled steels showed crack closure/opening behavior, and the crack opening load increased with increasing cold reduction rate. In addition, the difference in the fatigue crack propagation rates of cold-rolled steels was explained by the effective stress intensity factor range. From these results, the decrease of the fatigue crack growth by cold rolling was considered to be mainly due to the suppression of crack opening by cyclic softening near the fatigue crack tip.

The formation of CaS around CaO–Al₂O₃ in ferritic stainless steel was investigated. CaS was observed in the samples after heat treatment, although it was not detected in cast ingots. Al and S contents affect the precipitation of CaS because of the following reaction: 2[Al]+3[S]+3CaO=Al₂O₃+3CaS. The formation of CaS cannot be predicted by thermodynamic calculation. The growth of CaS during heat treatment is thought to be mainly controlled by S diffusion in steel. The factors for reducing CaS precipitation are discussed in this work.