该杂志为出版钢铁的特性、结构、表征和建模、加工、制造和环境问题以及相关工程材料的基本和技术方面提供了一个国际媒体。

ISIJ International is an international journal dedicated to the characteristics, structure, characterization, and modeling of steel, as well as processing, manufacturing, and environmental issues, covering both fundamental and technical aspects of related engineering materials.

ISIJ International 是一本国际性期刊，专注于钢铁的特性、结构、表征和建模，以及加工、制造和环境问题，涵盖相关工程材料的基础和技术方面。

ISIJ International

Experiments were carried out in a slag cleaning electric furnace to reduce magnetite in copper smelting slag using petro-diesel or biodiesel produced from waste cooking oil, and the reduction effects using the two reducing agents were compared. The pyrolysis experiments of petro-diesel and biodiesel were carried out in a fixed bed reactor to study their pyrolysis characteristics and explore the reduction mechanism of magnetite in copper smelting slag. The experimental results showed that the primary crystalline phases in copper smelting slag were magnetite [Fe3O4] and silicate. And the content of fayalite [Fe2SiO4] increased while the magnetite [Fe3O4] decreased after reduction by petro-diesel and biodiesel. In the process of copper smelting slag reduction, the petro-diesel and biodiesel firstly cracked into coke, H2, CO and CH4, and then reacted with the magnetite in the slag. The reduction effect of biodiesel was better than petro-diesel because more reducing gases can be generated through pyrolysis of biodiesel, and its price should be much lower than petro-diesel. Therefore, magnetite reduction in copper smelting slag using biodiesel produced from waste cooking oil should be recognized as an economical and environment-friendly process for waste management and application.

Bo Li

Yonggang Wei

Hua Wang

Yindong Yang

Reduction of Magnetite from Copper Smelting Slag using Petro-diesel and Biodiesel

Identifying surface defects is an essential task in the hot-rolled process. Currently, various computer vision-based classification and detection methods have achieved superior results in recognizing surface defects. However, defects typically exhibit irregular shapes caused by intra-class differences. Therefore, these two methods are unable to accurately identify the specific locations of the defects. To address this issue, this work proposes a U-shaped Encoder-Decoder framework called Resformer-Unet, which can effectively detect surface defects of hot-rolled strip at the pixel-level. In this framework, the Convolutional Neural Network (CNN) and Transformer work in parallel to extract multi-scale features from the image, which enhances the ability of network to capture both global and local information. Additionally, feature coupling modules are employed to fuse multi-scale features, thereby compensating for the information loss that occurs during down-sampling. On the SD-saliency-900 dataset for strip steel surface defect segmentation, Resformer-Unet achieves a mean Dice Similarity Coefficient (DSC) of 89.96% and an average Hausdorff Distance of 12.03%. These results outperform those of several advanced methods.

Kun Lu

Wenyan Wang

Xuejuan Pan

Yuming Zhou

Zhaoquan Chen

Yuan Zhao

Bing Wang

Resformer-Unet: A U-shaped Framework Combining ResNet and Transformer for Segmentation of Strip Steel Surface Defects

Resformer-Unet：结合ResNet和Transformer的U型框架用于带钢表面缺陷分割

Reasonable gas flow distribution plays a decisive role in the smooth operation of blast furnace smelting, but it is difficult to detect the gas flow distribution in blast furnace in real time. An intelligent prediction and identification system of central gas flow distribution based on infrared image of blast furnace and cross-beam temperature measurement is constructed(C-GFD). The system is mainly composed of two models, namely the image model and the prediction and recognition model. In the image model, three kinds of derived parameters, namely, central gas flow area, temperature and offset, are extracted by the image entropy and neighbourhood valley-emphasis (ENVE) Otsu, thermodynamic heat transfer and grey scale centroid algorithms, and then the statistical relationship between the change of image information and the distribution of gas flow is investigated. In the prediction and recognition model is established by the algorithms based on convolutional neural network long and short-term memory (CNN-LSTM) and Euclidean-weighted fuzzy C-mean clustering (E-FCM) to complete the prediction of the three types of derived parameters, and the prediction data is transferred to the recognition model to complete the recognition of the central gas flow distribution pattern. The test results show that the system provides real-time and reliable gas flow reference information for blast furnace operators with 95% accuracy in model prediction and more than 90% accuracy in pattern recognition of various types of central gas flow distribution.

Fu-min Li

Chang-hao Li

Song Liu

Xiao-jie Liu

Jun Zhao

Qing Lyu

System for recognizing gas flow distribution patterns in blast furnace centre based on computer vision

基于计算机视觉的高炉中心气体流分布模式识别系统

HIsarna is a new coal based smelting reduction process, which has the excellent features of using coal and fine hematite ore directly as raw materials instead of coke and pellet. In this context, the reduction kinetics of hematite ore fines in the smelting cyclone was studied in the laboratory scale. The gas-solid and gas-molten particle reduction behaviour were studied with a High-temperature Drop Tube Furnace (HDTF) and a combination of various characterization methods was used to track the kinetic behaviour such as chemical titration, optical microscope and Scanning Electron Microscope (SEM). A series of experiments with different reaction time (210–2 020 ms) has been conducted at different temperatures from 1 550 K to 1 750 K, thus enabling the kinetic study of the partially reduced hematite ore particles. It was found that a quantity of micro pores was formed during the reduction process mainly due to the loss of oxygen. The un-reacted shrinking core model could be used to describe both gas-solid particle reaction and gas-molten particle reaction.

Yingxia Qu

Yongxiang Yang

Zongshu Zou

Christiaan Zeilstra

Koen Meijer

Rob Boom

ISIJ Int.

Reduction Kinetics of Fine Hematite Ore Particles with a High Temperature Drop Tube Furnace

高温落管炉中细粒赤铁矿还原动力学

Grain growth processes are usually classified into two types. The first is a self-similar coarsening process, which is known as normal grain growth, while the second is characterized by the coarsening of a few grains at the expense of the surrounding matrix, and is known as abnormal grain growth. Although different mechanisms have been proposed for abnormal grain growth, the actual physical mechanism responsible for this phenomenon remains largely unknown. To inhibit normal grain growth in polycrystalline metals, dispersions of second-phase particles are often used. Using mean field analysis involving particle dispersions, Hillert and Humphreys predicted the condition where only abnormal grain growth occurs. In addition, Monte Carlo simulations on particle-assisted abnormal grain growth have been reported; however, the mechanism for this phenomenon has yet to be clarified. In this study, the abnormal grain growth caused by dispersed particles was reproduced using three-dimensional phase-field (PF) simulations. In particular, we investigated the influence of the particle dissolution rate on the intensity of abnormal grain growth. Furthermore, we evaluated the characteristics of individual grains exhibiting the maximum grain size at the end of the simulation. Our PF simulations revealed that size superiority under the initial condition is important for enhancing abnormal grain growth, as is the “growth environment,” i.e., the average grain size of adjacent grains that changes sequentially during the simulation. To deduce the effects of the pinning particles, the anisotropy in the interface properties was not considered in this study.

Yoshihiro Suwa

Kohsaku Ushioda

Phase-field Simulation of Abnormal Grain Growth due to the Existence of Second-phase Particles

第二相粒子存在下的反常晶粒长大相场模拟

In steel rail production, complex deformations can induce non-uniform changes in cross-sectional profiles along the rail's length, resulting in unevenness and safety implications. It is essential to perform dimensional testing to ascertain compliance with standard requirements. Currently, profile inspection results are manually evaluated, posing efficiency challenges and a lack of standardized criteria.To address this challenge, this paper proposes an online automatic steel rail segmentation and evaluation method (online-ASE) based on pattern matching and complex networks to enable automatic rail profile assessment. This method initially utilizes offline high-dimensional time series data for conducting Toeplitz Inverse Covariance-based Clustering (TICC) training and constructs a standard quality characterization pattern library through distinct inverse covariance structures between abnormal and normal high-dimensional quality characterization indicators of steel rails. When applied online, the Viterbi shortest path dynamic programming algorithm is utilized to match steel rail data with the pattern library, swiftly identifying anomalous rail segments. Additionally, the algorithm computes the contribution of steel rail quality parameters to the segmentation results using complex network betweenness centrality, thereby explaining the reasons for segment formation. These explanations provide a reference basis for subsequent steel rail repairs. Finally, the effectiveness of the proposed method is validated using real-world steel rail data from a specific steel factory in China.

Lingling Tong

Zhimin Lv

Jing Guo

Application of Online Automated Segmentation and Evaluation Method in Anomaly Detection at Rail Profile Based on Pattern Matching and Complex Networks

基于模式匹配和复杂网络的轨道轮廓异常检测中在线自动分割与评估方法的应用

This study focuses on the fracture mode of 201 austenitic steel at room temperature (RT) and at −40°C for one hour and three hours. The results reveal that at room temperature, the fracture is dominated by ductile behavior. At −40°C, the fracture mode is a mix of ductile and brittle behavior. Type 201 austenitic stainless steel has a low stacking fault energy value (about 16 mJ.m−2 at RT), leading to the activation of the transformation-induced plasticity (TRIP) effect. When the sample is soaked at −40°C for three hours, deformation-induced martensite transition (DIMT) formation with the volume fraction rises significantly to 19.9%. At −40°C for 3 hours, the alloy's impact energy absorption is reduced by 39%. The interaction of deformed austenite grains with previous austenite grain boundaries results in the formation of serrated grain boundaries in samples soaked at −40°C. Serrated grain boundaries prevent crack propagation and reduce crack expansion at the grain boundary during the fracture of this alloy. The width of the crack at serrated grain boundaries is 38% less than that of the straight grain boundary.

Pham Mai Khanh

Hoang Thi Ngoc Quyen

Effect of Deformation-induced α’-martensite and Serrated Grain Boundaries on The Deformation and Fracture Behavior of 201 Austenitic Stainless Steel at Low Temperature

变形诱导α’-马氏体和锯齿状晶界对201奥氏体不锈钢低温变形和断裂行为的影响

This study examines the effect of container size on coal briquette's internal structure using the Discrete Element Method. It found that when the frictional resistance between particle and wall was large and the inner diameter small, the difference in particle filling ratio between the upper and lower parts of the briquette was significant. Conversely, with a larger inner diameter, this difference nearly disappeared. The distribution of contact force indicated that the frictional force's inhibiting effect on force transmission lessened with a larger container's inner diameter. The study also revealed that the height of the container affects the briquette's internal structure, and these results can be summarized by the container's height to diameter ratio. Essentially, a larger ratio led to a linear increase in the difference in filling ratio between the upper and lower parts of the briquette.

Yuya Ono

Yoshiya Matsukawa

Yohsuke Matsushita

Takahiro Shishido

Shohei Wada

Ryuichi Kobori

Noriyuki Okuyama

Hideyuki Aoki

Effect of the Size of Coal Briquette on Its Internal Structure

煤球尺寸对其内部结构的影响

Thermal mixing during the gas stirring operation and arc heating in a steel ladle is analyzed through the modern tools of a physical model using PIV (Particle Image Velocimetry) and thermal PLIF (Planar Laser Induced Fluorescence), whose velocity and temperature fields were used to fine-tune and validate a multiphase Eulerian two-phase mathematical model. Agreement on both fluid dynamics and thermal evolution is reasonably good between experiments and the predictions obtained by the mathematical model of the physical model. The analysis coming from the numerical model validated by the physical model measurements included the thermal mixing and energy efficiency of single nozzle injection in centric and eccentric (4/5R) gas injection. It turned out that energy efficiency in the centric gas injection is 20% more efficient than in eccentric injection. Then, under the same heat flux provided, the maximum temperature of the water in the centric gas injection would be higher than the maximum temperature reached in the eccentric mode with the same gas flow rate. Good heat transfer happens when the heat source impinges in a fluid region with high circulation and turbulent dispersion.

Adrián Amaro-Villeda

Abhishek Dutta

Marco Guevara-Castillo

Luis Enrique Jardón-Pérez

Marco Aurelio Ramírez-Argáez

Thermal mixing analysis in a ladle utilizing physical and numerical modeling through planar laser-induced fluorescence (PLIF) technique

利用平面激光诱导荧光（PLIF）技术通过物理和数值建模进行钢包内热混合分析

Air-quenched electric arc furnace slag (AEAFS) is a black sphere or spheroid particle prepared by an air quenching theology using electric arc furnace steelmaking slag as raw materials, possessing the characteristics of small particle size, moderate density and high hardness Combined with the tight supply and demand of the existing abrasive market and the continuous increase in price, AEAFS is tried to be used as a free abrasive for sandblasting processing according to its physical characteristics. In order to make sure that the AEAFS meets the requirement of free abrasive blasting, it is necessary to conduct a comprehensive and in-depth analysis of its physical and chemical properties. The research shows that the AEAFS is a spherical particle with weak magnetism and particle size being mainly 2.8 mm (accounting for more than 90%). Its Vickers hardness is in the range of 600-1000HV; its compressive strength is between 20 and 465N and increases first and then decreases with particle size. The water content is more than 0.019%, except that the particle size is less than 0.5mm. All the others meet the requirements of ISO-11126-6: 2018 standard. The content of f-CaO is between 1.122% and 1.612% increasing with the particle size, AEAFS has good chemical stability and weak acid resistance. In summary, AEAFS meets the performance requirements of the medium used in the sandblasting process and is a potential alternative product for sandblasting abrasives.

Wenlong Lin

Wei Zhang

Shaopeng Gu

Wang Hui

Jingjing Pei

Ruijing Sun

Donghui Liu

Chao Liu

Hongwei Xing

Physicochemical Properties of Air-Quenched Electric Arc Furnace Slag as Free-State Sandblasting Abrasives and Application Potential Analysis

空气淬火电弧炉渣作为自由状态喷砂磨料的物理化学性质及应用潜力分析

Abbreviation	ISIJ Int.
Journal Impact	1.51
Quartiles(Global)	METALLURGY & METALLURGICAL ENGINEERING(Q2)

ISSN	0915-1559, 1347-5460
h-index	132

Publication Information	Publisher: Iron and Steel Institute of Japan，Publishing cycle: Monthly，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 1989，Proportion of original research papers: 95.62%，Self Citation Rate:12.50%， Gold OA Rate: 98.52%
Journal Fees	$Details
Average review cycle	网友分享经验：平均5.0个月
Average recruitment ratio	网友分享经验：容易

ISIJ International

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