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Tetsu-to-Hagané Vol. 109 (2023), No. 11

ISIJ International
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ONLINE ISSN: 1883-2954
PRINT ISSN: 0021-1575
Publisher: The Iron and Steel Institute of Japan

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Tetsu-to-Hagané Vol. 109 (2023), No. 11

In-situ Observation of Sintering Interface between Al2O3 Particle / Single Crystalline Al2O3 Plate through Single Crystalline Al2O3 Plate

Masashi Nakamoto, Toshihiro Tanaka

pp. 847-856

Abstract

In a continuous casting process, the clogging of the immersion nozzle with inclusions occurs as a result of the adhesion, agglomeration, and coalescence of inclusions. The most effective way to understand the behavior of inclusions, i.e. oxide particles, in a liquid steel is the in-situ observation. To our knowledge, however, there is no research on the in-situ observation of the behavior of oxide particles in a liquid steel. In the present work, therefore, the in-situ observation method for sintering interface between Al2O3 particle / single crystalline Al2O3 plate by laser microscope through single crystalline Al2O3 plate is proposed. First, the in-situ observation of sintering interface between Al2O3 particle and single crystalline Al2O3 in Ar atmosphere is conducted to verify the observation method for the interface through single crystalline Al2O3 plate. Then, the in-situ observation of sintering interface between Al2O3 particle and single crystalline Al2O3 in a liquid Ag is challenged. The observations for sintering interface between Al2O3 particle and single crystalline Al2O3 plate in Ar gas atmosphere and a liquid Ag are achieved by our proposed method. It is verified that the growth of sintering interface in liquid Ag is faster than that in an Ar gas atmosphere. This finding indicates that the non-wetting by liquid Ag of alumina particles promotes the growth of sintering interface in liquid Ag.

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In-situ Observation of Sintering Interface between Al2O3 Particle / Single Crystalline Al2O3 Plate through Single Crystalline Al2O3 Plate

An Optimization for Ore Stockyard Layout Operation Schedules Using Multi-Start Greedy Algorithm

Akira Kumano, Yusuke Yoshinari, Osamu Yamaguchi, Toru Miyazawa

pp. 857-864

Abstract

We have developed an optimal scheduling method for the highly efficient management of multiple stockpiles of iron ore in the stockyard to significantly improve both yard logistics and the operational stability of steelworks.

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An Optimization for Ore Stockyard Layout Operation Schedules Using Multi-Start Greedy Algorithm

Measurement of Oil Film Thickness Distribution in Roll Bite during Cold Rolling Using Quantum Dots

Masahiro Shimura, Daisuke Kasai, Takayuki Otsuka, Naoki Yamashita, Tomoko Hirayama

pp. 865-879

Abstract

During the cold rolling of flat steel products, lubrication is critical to achieve stable rolling. However, the oil film thickness distribution in the roll bite and its effect on friction between the work roll and strip have not been clarified thus far. This study aimed to elucidate the relationship between the oil film thickness distribution and friction by focusing on the rolling oil viscosity and steel grades, because they significantly affect the friction between the work roll and strip. Rolling oil was prepared with quantum dots as the fluorescent additive and used in rolling experiments to identify its distribution. Furthermore, cold rolling experiments were conducted using two types of oil with different viscosities and three different steel grades, namely low-carbon steel (LCS), high-strength steel (HSS), and advanced high-strength steel (AHSS) with tensile strengths of 270, 590, and 1180 MPa. Subsequently, the oil film thickness distribution on the steel strip surface was visualized using quantum dots by fluorescence microscopy. It was demonstrated that the higher the tensile strength of the steel or the higher the oil viscosity, the wider the rolling oil distribution on the strip surface. Numerical analyses revealed that the rolling oil distribution on the steel sheet surface was more widespread for AHSS and HSS than for LCS. The high surface pressure between the roll and steel plate may have increased the oil leaching area by increasing the oil viscosity. These findings demonstrate that rolling oil permeation from the oil-pits reduces the friction between the work rolls and strip.

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Measurement of Oil Film Thickness Distribution in Roll Bite during Cold Rolling Using Quantum Dots

Effects of Material Properties on the Teeth Accuracy of Spline Shafts in Cold Form Rolling

Hiroaki Shinto, Shinya Ienaka, Yohei Matsumoto, Hiroshi Utsunomiya

pp. 880-889

Abstract

Effects of material properties on the teeth accuracy of spline shafts processed by cold form rolling with rack dies have been experimentally investigated. The materials used were medium-carbon steel bars processed by four kinds of heat treatments. Three of them were quenched from 850°C followed by tempering at 570°C, 630°C,700°C respectively, and the fourth was annealed at 600°C. The remarks obtained by this study are as follows. (1) Teeth accuracy is improved with increasing tempering temperature, while the steel annealed at 600°C has almost the same teeth accuracy as the steel tempered at 700°C. (2) Teeth accuracy shows high correlation with cumulative pitch deviation from the theoretical value. (3) The low teeth accuracy is due to that of the follower side teeth which shows lower formability than the driven side. (4) In order to find a material property which affects teeth accuracy, material flow inside the necking portion in the tensile test was focused from the fact that material flows of the follower side teeth are similar with that of the necking portion. (5) Cup and cone type fracture was observed in the tensile test. The value NPC defined as the width of tapered portion divided by outer diameter of tapered portion is proposed as an index of the formability in cold form rolling, and the reason why the teeth accuracy is improved with increasing this index is investigated on the basis of experimental results.

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Effects of Material Properties on the Teeth Accuracy of Spline Shafts in Cold Form Rolling

Thermodynamic Analysis of Precipitation Behaviors in Hot-Rolled Ti-stabilized Interstitial-Free Steel Sheets

Michitoshi Saeki, Takako Yamashita

pp. 890-897

Abstract

Thermodynamic calculations were performed on the precipitation behavior in the hot-rolled Ti-stabilized Interstitial Free (IF) steel sheets. By combining the thermodynamic parameters of Ti4C2S2 evaluated by first-principles calculations with previously reported parameters, parameters were developed that allow the calculation of the precipitation behavior of IF steels during hot rolling. The calculated precipitation behavior using those thermodynamic parameters is in relatively good agreement with the TEM observation results in hot-rolled IF steel sheets. The results of thermodynamic calculations suggest that the precipitation behavior of IF steel sheets during hot rolling depends on the amounts of Mn. These results are considered to be due to the decreased excess S in steel sheets as a result of the increase in precipitation of sulfides containing Mn.

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Thermodynamic Analysis of Precipitation Behaviors in Hot-Rolled Ti-stabilized Interstitial-Free Steel Sheets

Microstructure Estimation by Combining Deep Learning and Phase Transformation Model

Satoshi Noguchi, Syuji Aihara, Junya Inoue

pp. 898-914

Abstract

In material design, the establishment of process–structure–property relationship is crucial for analyzing and controlling material microstructures. For the establishment of process–structure–property relationship, a central problem is the analysis, characterization, and control of microstructures, since microstructures are highly sensitive to material processing and critically affect material’s properties. Therefore, accurately estimating the morphology of material microstructures plays a significant role in understanding the process–structure–property relationship. In this paper, we propose a deep-learning framework for estimating material microstructures under specific process conditions. The framework utilizes two deep learning networks: Vector Quantized Variational Autoencoder (VQVAE) and Pixel Convolutional Neural Network (PixelCNN). The framework can predict material micrographs from the transformation behavior given by some physical model. In this sense, the framework is consistent with the physical knowledge accumulated in the field of material science. Importantly our study demonstrates qualitative and quantitative evidences that incorporating physical models enhances the accuracy of microstructure estimation by deep learning models. These results highlight the importance of appropriately integrating field-specific knowledge when applying data-driven frameworks to materials design. Consequently, our results provide a foundation for integrating data-driven methods with the accumulated knowledge in the field. This integration holds great potential for advancing material design through deep learning.

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Microstructure Estimation by Combining Deep Learning and Phase Transformation Model

Flow Stress of Duplex Stainless Steel by Inverse Analysis with Dynamic Recovery and Recrystallization Model

Kyunghyun Kim, Hyung-Won Park, Sheng Ding, Hyeon-Woo Park, Jun Yanagimoto

pp. 915-926

Abstract

To obtain the flow stress in duplex stainless steel, a duplex flow model is proposed that applies a rule of mixtures with the relationship between the volume fractions of austenite and ferrite. The model includes the saturated stress ratio λ and the volume fractions of austenite and ferrite at various temperatures. It considers the mechanical deformation and microstructural evolution with dynamic recrystallization (DRX) and dynamic recovery (DRV) of the two phases during hot working. To confirm the validity of the proposed model and new inverse analysis method, hot compression experiments were performed at deformation temperatures of 1050, 1150, and 1250°C and strain rates of 0.1, 1, and 10 s−1 with SUS329J4L, which is an austenite-ferrite duplex stainless steel. According to the flow curves, the softening rate from the peak stress was steeper with decreasing temperature from 1250 to 1050°C, corresponding to estimated austenite volume fractions from 33% (1250°C) to 61% (1050°C). Microstructural heterogeneity between DRX in the austenite and DRV in the ferrite was observed at deformation temperatures from 1050 to 1250°C, confirming that a clearly different restoration mechanism occurred in the two phases.

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Flow Stress of Duplex Stainless Steel by Inverse Analysis with Dynamic Recovery and Recrystallization Model

Quantification of Changes in Lattice Defect Density in BCC Iron during Plastic Deformation Using Electrical Resistivity Measurements

Soichiro Takenaka, Ryohei Takahashi, Kazuhiro Ishikawa, Yoji Miyajima

pp. 927-933

Abstract

Change in lattice defects density in bcc pure iron due to tensile deformation was quantified by using both electrical resistivity measurements and X-ray diffraction (XRD). As bcc pure irons, ultra-low carbon steel (ULCS) and interstitial free (IF) steel are used as the model specimen. Dislocation density evaluated using Williamson Hall method with XRD shows the saturation with the value of around 3.7×1015 m−1 for ULCS and around 1.4×1015 m−1 for IF steel after plastic strain after ~5%. Increase in electrical resistivity was observed with increasing plastic strain. Consequently, increase in vacancy concentration occurs with increasing plastic strain of around 0.3, such as, 2.6×10−5 for ULCS and 3.4×10−5 for IF steel. Additionally, the migration of carbon atoms from grain interior to grain boundary via dislocation might occur at the initial stage of plastic deformation in ULCS.

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Quantification of Changes in Lattice Defect Density in BCC Iron during Plastic Deformation Using Electrical Resistivity Measurements

Mechanism of Al Coordination Change in Alkaline-earth Aluminosilicate Glasses: An Application of Bond Valence Model

Sohei Sukenaga, Koji Kanehashi, Hiroki Yamada, Koji Ohara, Toru Wakihara, Hiroyuki Shibata

pp. 934-939

Abstract

Aluminum cations are generally present in four-fold ([4]Al3+) or five-fold coordination ([5]Al3+) in aluminosilicate slags, where the concentration of [5]Al3+ varies depending on the type of charge compensator, for example, Mg2+ and Ca2+. Although it has been reported that the amount of [5]Al3+ species increases with the replacement of CaO with MgO in the CaO–MgO–SiO2–Al2O3 system, the detailed mechanism underlying the change in the local structure near the aluminum cations remains unclear. Because the residual negative charge on the bridging oxygen between [4]Si4+ and [5]Al3+ ([4]Si4+–OBO[5]Al3+) is larger than that of [4]Si4+–OBO[4]Al3+, it is essential to understand the positive charge contributions of alkaline-earth cations to compensate for these negative charges on the bridging oxygens. In the present study, the valence of a single chemical bond near Mg2+ and Ca2+ cations in the chosen aluminosilicate glasses was determined using a simple empirical model, which enabled calculation of the bond valence from the observed interatomic distance of near alkaline-earth cations by synchrotron X-ray total scattering. Magnesium cations had a larger average bond valence (+0.39) than calcium cations (+0.31). The difference in the positive charge contribution from Mg2+ and Ca2+ should explain the variation in the coordination number of aluminum cations.

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Mechanism of Al Coordination Change in Alkaline-earth Aluminosilicate Glasses: An Application of Bond Valence Model

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