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ONLINE ISSN: 1347-5460
PRINT ISSN: 0915-1559
Publisher: The Iron and Steel Institute of Japan

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ISIJ International Advance Publication

Thermal stability of retained austenite with heterogeneous composition and size in austempered Fe-2Mn-1.5Si-0.4C alloy

Miku Watanabe, Goro Miyamoto, Yongjie Zhang, Satoshi Morooka, Stefanus Harjo, Yasuhiro Kobayashi, Tadashi Furuhara

Abstract

The mechanical properties of TRIP steels depend on heterogeneities of chemical composition and grain size in the retained γ structure, although these heterogeneities have not been characterized in detail. Therefore, in this study, we quantitatively investigate the inhomogeneous carbon concentration and grain size distribution, and its effects on the thermal stability of the retained γ in Fe-2Mn-1.5Si-0.4C (mass%) TRIP steel using FE-EPMA, EBSD, Mössbauer spectroscopy, and in-situ neutron diffraction during bainitic transformation at 673 K. In-situ neutron diffraction experiments detects high-carbon γ evolving during bainite transformation, in addition to the original γ, and the time variation of the volume fraction of high-carbon γ agrees well with the fraction of γ retained at room temperature. Williamson-Hall analysis based on peak width suggests that heterogeneity of carbon content exists even within the high-carbon γ. Compositional analysis using FE-EPMA and three-dimensional atom probe directly revealed that fine filmy γ was highly enriched with carbon compared to larger blocky γ, and the carbon content in blocky γ decreases with increasing blocky γ size. DICTRA simulation qualitatively reproduces the size dependency of carbon enrichment into γ. It was also found that γ tends to be retained at higher carbon content and smaller γ grain size since the smaller grain size directly improves thermal stability and the smaller γ size further contributes to the thermal stability via enhanced carbon enrichment.

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Thermal stability of retained austenite with heterogeneous composition and size in austempered Fe-2Mn-1.5Si-0.4C alloy

Effect of the Jet from Top Lance on Slag Foaming Behavior in Basic Oxygen Furnace process

Shinya Miura, Teppei Tamura, Ken-ichiro Naito

Abstract

As for steelmaking process such as basic oxygen furnace (BOF) and electric arc furnace (EAF), slag foaming consists of introducing gas bubbles into molten metal and slag by chemical reaction. In the case of the BOF process, excessive foaming is over the converter capacity, a phenomenon called "slopping". Slopping reduces yield and equipment lifespan and increases production time. It is therefore important to control slag foaming properly. In previous studies by other investigators, the jet from top lance in BOF process effectively suppresses slag foaming. However, it is not obvious which mechanism of the jet from top lance is effective to suppress slag foaming, and its quantitative effect has not been reported. To clarify the relationship between slag foaming and the jet from top lance, the effects of the number of nozzle holes and lance height on the slag foaming were investigated by using a converter-shaped water-model device and test converter. The experimental results indicated that slag foaming height decreased as the number of nozzle holes increased. Also, slag foaming height changed instantly with the change in lance height, e.g., slag foaming height decreased as lance height increased, and vice versa. The foaming suppression mechanism of the jet from top lance is the entrainment of foaming slag into the jet. Consequently, slag foaming model that takes the effect of the jet from top lance into account is proposed. And it enables to predict the change in slag foaming height with time.

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Effect of the Jet from Top Lance on Slag Foaming Behavior in Basic Oxygen Furnace process

Quantitative reduction of iron under nitrogen atmosphere for potassium dichromate titration

Yuto Kadowaki, Yoko Yanagihara, Arinori Inagawa, Nobuo Uehara

Abstract

Total iron contents in iron ores have been accurately determined by JIS M 8212, in which iron ions in digested solutions of iron ores are reduced to divalent prior to redox titration. It is necessary for the iron reduction process that no reducing chemicals other than iron(II) in the decomposition solutions must not remain after the reduction with titanium(III). However, the redox reactions concerning the chemical species present in the decomposition solution has not been completely elucidated at the present time. In this paper, the redox reactions that occurred in the decomposition solution during the iron reduction in JIS M 8212 were studied by potentiometry and spectrophotometry under nitrogen atmosphere. The redox reaction of tin(II)/(IV) was very slow, causing significant effects on identifying the end point of the indicator for the iron reduction. The copper chloro-complexes were reduced with titanium(III) at a potential higher than that of indigo carmine used as a redox indicator, so that the reduced copper(I) gave a positive error to the potassium dichromate titration. The pentavalent vanadium was reduced with titanium (III) to form a complex with titanium, which also interfered with the potassium dichromate titration positively. To avoid these interferences, titanium(III) chloride was stoichiometrically added to the reaction mixture after addition of tin(II) chloride under nitrogen atmosphere so as to reduce only iron to divalent prior to the following redox titration. Combination of the proposed protocol with the potassium dichromate titration could successfully determine the iron content of certified reference materials of iron ores.

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Quantitative reduction of iron under nitrogen atmosphere for potassium dichromate titration

Determination of the degree of sodic modification of bentonite using response surface analysis

Wei Mo, Yuxin Feng, Longlin Zhou, Jinlin Yang, Xiujuan Su, Jinpeng Feng

Abstract

Sodium modification is an effective approach for enhancing the properties of bentonite and reducing its usage in pellets. However, due to limited research, the relationship between the physicochemical properties of bentonite and its green ball properties remains unclear, and the optimal degree of modification for bentonite has rarely been discussed. Therefore, this paper proposes a novel research idea: to exploring the correlation between the five most commonly used indexes for evaluating the physicochemical properties of bentonite (water absorption, methylene blue index, swell capacity, colloid index, and cation exchange capacity) and the most frequently used evaluation indexes for assessing green ball performance (drop strength), in order to determine the optimal degree of sodium modification of bentonite for pellets. The response surface methodology was employed in this paper to investigate the quantitative relationship between the five indexes and the green ball drop strength. The results demonstrate that when the drop strength of the green ball reaches its optimal level, the five commonly used indicators of bentonite are as follows: water absorption is 545.27%, methylene blue index is 22.94g/100g, swell capacity is 72.36ml/g, colloid index is 35.95ml/g, and cation exchange capacity is 68.93mmol/100g. Under these conditions, it has been the predicted value for green ball drop strength is determined to be 12.88, which exceeds the maximum value in the experimental conditions by 48.05%. The study determined the optimal degree of sodium modification for bentonite in pelletizing, providing valuable guidance for optimizing the properties of bentonite.

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Determination of the degree of sodic modification of bentonite using response surface analysis

Recycling Process for Net-Zero CO2 Emissions in Steel Production

Ryota Higashi, Daisuke Maruoka, Yuji Iwami, Taichi Murakami

Abstract

The iron and steelmaking industry must focus on neutralizing CO2 emissions. One solution involves using hydrogen as a reducing agent for iron ore. However, carbon is an essential element as primary steel is produced by refining molten carbon-saturated iron (hot metal). Ironmaking processes applying CO2 capture and utilization have been suggested; however, they are limited to the reduction process. To satisfy the demand for primary steel production with net-zero CO2 emissions, a new carbon recycling ironmaking process capable of producing hot metal must be considered. This study proposes a carbon recycling ironmaking process using deposited carbon-iron ore composite (CRIP-D). In the CRIP-D process, hot metal is produced by using the solid carbon recovered by reforming exhaust gas as reducing and carburizing agents. Moreover, using the recovered solid carbon, iron oxides are reduced more rapidly, and reduced iron is melted at a lower temperature than that using fossil fuel-derived carbon. This means carbon-neutral steel can be produced more efficiently than conventional ironmaking processes. Using proven technologies, following hot metal production, primary steel can be produced while minimizing the burden on the steel mills for converting equipment. Thus, true carbon-neutral primary steel is feasible using the proposed CRIP-D process.

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Recycling Process for Net-Zero CO2 Emissions in Steel Production

Error analysis of green pellet size distribution measurement on conveyors using simulation method

Shuyi Zhou, Xiaoyan Liu

Abstract

3D vision technologies have been widely used in metallurgy industry to measure particle size distribution (PSD) of green pellets on conveyor. However, 3D camera only captures the point clouds of surface pellets, and algorithms measure the surface PSD. To what extent the measured surface PSD can reflect whole PSD is a question that hasn't been answered yet. In the present work, a simulation method is proposed to analyze the PSD measurement error of green pellets. First, the motion process of green pellets on conveyor is simulated by discrete element method to obtain PSD of whole pellets; then, a transformation method is proposed to generate point clouds of simulated surface pellets, and region growing-based method is adopted to measure the PSD of surface pellets; finally, the PSD measuring error can be obtained by comparing surface PSD and whole PSD of pellets. Error analysis of green pellet size distribution measurement on conveyors is conducted, in aspects of camera location, patch number of point clouds, thickness as well as size distribution of pellet bed. Results illustrate that although the PSD measuring error (up to 12.3%) cannot be neglected when camera is installed above conveyor, it can be effectively reduced by increasing the patch number of captured point clouds (reduced by more than 7.4%) or installing camera near discharge of conveyor (reduced to less than 3.1%).

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Error analysis of green pellet size distribution measurement on conveyors using simulation method

Collaborative optimization model of blast furnace raw materials and operating parameters based on intelligent calculation

Song Liu, Weijian Feng, Jun Zhao, Zhiwei Zhao, Xiaojie Liu, Ran Liu, Qing Lyu

Abstract

Aiming at the problem of coadjustment of blast furnace raw materials and operation parameters, this paper proposes a cooptimization model of blast furnace batching that integrates Random Forest and NSGA-Ⅲ (Non-dominated Sorting Genetic Algorithm III) algorithm. First, blast furnace field data were collected for a two-year time span, and a predictive model for CO2 emissions and blast furnace permeability was constructed using the Random Forest algorithm; taking the goodness of fit (R2), mean square error (MSE) and mean absolute error (MAE) as the evaluation indexes, the R2 of the two prediction models obtained reached 0.93 and 0.96 respectively, and the MSE and MAE tended to be close to the zero value. Then, NSGA-Ⅲ was used to establish the blast furnace batching optimization model to optimally solve the batching scheme and the corresponding blast furnace operating parameters by taking the lowest batching cost, the lowest carbon dioxide emission and the maximum blast furnace permeability as the objective function, and the composition requirement of raw materials and the range limitation of operating parameters as the constraints; finally, the model was validated using the actual on-site data, and the application results showed that the output of the model conformed to the Finally, the results show that the model output meets the composition requirements and obtains a lower-cost dosage scheme than the original dosage ratio; moreover, this scheme corresponds to a blast furnace with less carbon dioxide emission, better blast furnace permeability and less slag. Therefore, the model can provide an effective reference for field operators to optimize blast furnace batching and operation.

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Collaborative optimization model of blast furnace raw materials and operating parameters based on intelligent calculation

An Error Correction Method Based on CBR for End Temperature Prediction of Molten Steel in Ladle Furnace

Dongfeng He, Chengwei Song, Yuanzheng Guo, Kai Feng

Abstract

Accurately predicting the end temperature of molten steel is significant for controlling ladle furnace (LF) refining. This paper proposes an error correction method called EC-CBR based on case-based reasoning (CBR) to reduce errors in the prediction models caused by discrepancies between actual production data and training data. The proposed method combines the incremental learning advantage of CBR with the ability of other models to fit nonlinear relations. First, a prediction model is established, and historical heats similar to the new heat are retrieved by CBR. Then, the model error of the new heat is calculated by employing the errors of similar heats. The prediction result is calculated by subtracting the error from the predicted value. Testing and comparison are conducted on the models (support vector regression, backpropagation neural network, extreme learning machine and mechanism model) and general CBR using actual production data. Results show that the EC-CBR is effective for both data-driven and mechanism models, with an increase of approximately 5% in hit rate within the range of ±5°C for data-driven models and an increase of 21.73% for mechanism model. Moreover, the corrected data-driven models show higher accuracy than the general CBR, further proving the effectiveness of the proposed method.

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An Error Correction Method Based on CBR for End Temperature Prediction of Molten Steel in Ladle Furnace

Simulation and Application of O2-N2 Mixing Top-blowing Method in MURC Steelmaking Process

Fuhai Liu, Chen Sun, Rong Zhu, Kai Dong, Guangsheng Wei, Ruiguo Bai

Abstract

The O2 and N2 mixing top-blowing method could effectively improve the mixing degree and suppress the temperature increase rate of the molten bath in vanadium extraction converter. In this paper, four kinds of top-blowing lances designed by an extra N2 flow rate and various Mach numbers have been investigated by a series of water experiments and numerical simulations. On the basis of result, the mixing time was first increased and then decreased with the increase of lance height, and the lance height of 1400mm obtained the longest mixing time. There were two high-velocity regions generated by impaction of top-blowing jets and stirring of bottom-blowing bubbles. Simultaneously, there were two low-velocity regions formed by the block of furnace wall, and one low-velocity region formed by the local eddy. Comparing with the current top-blowing lance, all three new kinds of top-blowing lances obviously improved the kinetic condition and impaction cavity area of molten bath, which would further be improved with a larger design Mach number. Therefore, an appropriate top-blowing lance had been selected in the industrial application research, which achieved a shorter melting time and a faster vanadium extraction rate, in contrast to the current lance.

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Simulation and Application of O2-N2 Mixing Top-blowing Method in MURC Steelmaking Process

OPTIMIZATION OF DISSIMILAR ASS-DSS SPOT WELDED JOINTS ON TENSILE SHEAR FRACTURE LOAD

Vignesh Krishnan, Velmurugan Paramasivam

Abstract

Austenitic Stainless Steel (ASS) and Duplex Stainless Steel (DSS) are joined to optimize the Resistance Spot Welding (RSW) process parameters and to predict the parametric influence on the response of Tensile Shear Fracture Load (TSFL). The Response Surface Methodology (RSM) is an optimization technique is used in this research to develop the satisfactory quadratic mathematical model and to predict the response. The optimal parameters and their levels are found and reported as follows: welding current = 9 kA, welding time = 0.18 seconds and electrode tip radius = 3 mm. The actual and predicted values of TSFL for the optimized parameters are 17.6 kN and 17.9 kN respectively. The developed quadratic model is efficiently predicted the response with an average error percentage of 2.18. The significant and insignificant terms in the models has been identified by 95% confidence level using ‘p' test. The insignificant terms are removed from the model and the ANOVA table is formulated only with the significant terms. Significance or effect of each term in the ANOVA table is identified by calculating the percentage of contribution and noticed that welding current has the highest significance (46%) on TSFL. The macroscopic examination confirmed that the larger nugget is observed during the maximum welding current due to the high heat generation. Also, the variation in TSFL against the process parameters are observed as same as nugget length, because, TSFL and nugget length are perfectly correlated.

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OPTIMIZATION OF DISSIMILAR ASS-DSS SPOT WELDED JOINTS ON TENSILE SHEAR FRACTURE LOAD

Prediction of Silicon Content of Hot Metal in Blast Furnace Based on Optuna-GBDT

Lili Meng, Jinxiang Liu, Ran Liu, Hongyang Li, Zhi Zheng, Yao Peng, Xi Cui

Abstract

The silicon content of hot metal is a key index for the determination of blast furnace status, and accurate prediction of the silicon content of hot metal is crucial for blast furnace ironmaking. First, 10992 sets of blast furnace data obtained from the site of an iron and steel enterprise were preprocessed. Then, 22 important feature parameters related to the silicon content of hot metal were screened by feature engineering. Finally, the hyperparameters of the Gradient Boosting Decision Tree (GBDT) algorithm model were optimized with the help of the Optuna framework, and the Optuna-GBDT model was established to predict the silicon content of hot metal. The experimental results show that compared with the Bayesian algorithm and the traditional stochastic search method, the Optuna framework can achieve better hyperparameter optimization with fewer iterations and smaller errors.The Optuna-GBDT model performs better in predicting the silicon content of hot metal compared with the optimized Random Forest (RF), Decision Tree and AdaBoost models, and the prediction results are basically in line with the actual values, with the mean absolute error (MAE) of 0.0094, the root mean square error (RMSE) of 0.0152, and the coefficient of determination (R2) of 0.975.The experimental results verified the validity and feasibility of establishing the Optuna-GBDT model to predict the silicon content of hot metal, which provides a reliable tool for iron and steel enterprises and helps to optimize the ironmaking process, improve production efficiency and product quality.

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Prediction of Silicon Content of Hot Metal in Blast Furnace Based on Optuna-GBDT

In-Situ Observation of the Modification Behavior of Alumina Inclusions in a Calcium-treated Steel

Guojun Chen, Ying Ren, Minghui Wu, Weijian Wang, Lifeng Zhang

Abstract

In the current study, a novel laboratory experiment and a kinetic calculation were proposed to analyze the modification behavior of alumina inclusions in the molten steel. To obtain the shape and composition of a single Al2O3 inclusion at different times during the modification process, confocal scanning laser microscopy experiments were conducted to track the evolution of the Al2O3 inclusion particle on the surface of Ca-treated steel. Then, the composition evolution of the Al2O3 inclusion particle during the modification process was predicted using a kinetic model. It was assumed the product layer was homogeneous. The diffusion of dissolved [Ca], [Al], and [O] crosses through the inclusion-steel interface was considered. Experimental results agreed well with kinetic calculated results. Meanwhile, the kinetic model was used to analyze the modification behavior of Al2O3 inclusions in steel with various influence factors including the [Ca] content in steel, the [Al] content in steel, and the initial size of inclusions.

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In-Situ Observation of the Modification Behavior of Alumina Inclusions in a Calcium-treated Steel

Effect of Wettability on Droplet Agglomeration in Two Immiscible Liquids

Sunglock Lim, Ikuru Hatakeyama, Masashi Nakamoto, Takeshi Yoshikawa, Toshihiro Tanaka

Abstract

The agglomeration of droplets dispersed in an immiscible liquid is often an issue in metallurgical processes. To determine the effect of wettability on droplet agglomeration, we conducted (1) wettability and (2) agglomeration experiments using immiscible liquid paraffin and glycerin aqueous solution. In the wettability experiment, a droplet of one liquid was settled on the other liquid and its shape was observed. The glycerin droplet was wrapped by liquid paraffin, while the paraffin droplet spread on the surface of the glycerin solution. Therefore, liquid paraffin wetted the glycerin droplet, while glycerin solution did not wet the paraffin droplet. In the agglomeration experiment, after the droplet settled or floated in the other liquid layer to arrive at the boundary between the two liquid layers, we measured the time required for droplet agglomeration in its liquid layer. The agglomeration of paraffin droplets from the glycerin solution was faster than that of glycerin droplets from liquid paraffin, indicating that non-wettability of droplets accelerated agglomeration.

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Effect of Wettability on Droplet Agglomeration in Two Immiscible Liquids

Analysis and prediction of sticker breakout based on XGBoost forward iterative model

Yu Liu, Zhixin Ma, Xudong Wang, Yali Gao, Man Yao, Zhiqiang Xu, Miao Yu

Abstract

All 61 sticker breakouts and 183 false sticker breakouts were obtained based on the on-line mould monitoring system during the conventional slab continuous casting. The 16-dimensional temperature characteristics and temperature velocity characteristics of the sticker breakout were extracted. The sticker breakout recognition based on the XGBoost forward iterative model was developed and optimized by the mean square error algorithm. The results show that the prediction probability of the sticker breakout after optimization is in the range of 0.72∼1.00. The smallest output value 0.5 higher than that before optimization. When the threshold is set to 0.65, the optimized XGBoost model can correctly predict all sticker breakouts and has a 99.5% accuracy rate. The XGBoost model has a stronger generalization ability and higher prediction accuracy, which promotes the intelligent production of continuous casting.

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Analysis and prediction of sticker breakout based on XGBoost forward iterative model

Effect of mixed gas ratio on cooling performance of impinging jets

Hirokazu Kobayashi, Gentaro Takeda, Kenji Katoh, Tatsuro Wakimoto

Abstract

Gas jet cooling is widely used because the device is simple, oxidation can be prevented, and a uniform cooling capacity can be obtained with thin steel sheets. Because the gas jet cooling ability is affected by the physical properties of the gas such as the mixed gas ratio, a quantitative evaluation of the influence of these factors is very important. However, few studies concerning prediction of the cooling capacity of mixed gas jets in atmospheres with different concentrations have been published.

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Effect of mixed gas ratio on cooling performance of impinging jets

The evolutions of microstructure and mechanical properties of the welding heat-affected zone in 800MPa-grade complex phase steel

Yue Wang, Xianchun Dong, Yang Yu, Kaiguang Hu, Bing Yu, Shuhai Chen

Abstract

A thermomechanical simulator Gleeble 3800 was used to simulate the thermal cycles experienced by various heat-affected zones (HAZ) during the welding process. The influence of peak temperature (Tp, 500°C~1320°C) on the hardness, microstructure, precipitates, and properties of complex steel 780FB with microalloyed elements Ti, Nb, and V was systematically studied. The contributions of dislocation strengthening, precipitation strengthening, fine grain strengthening, and phase transformation strengthening increments to strength changes of samples after different thermal cycles were quantified, and the calculated results were found to be consistent with the experimental data. Compared with 780FB, there was little change in microstructure and properties when Tp was 500°C. When Tp was 650°C, the increase in VC density from 43/μm2 to 288/μm2 caused the enhancement of hardness and strength. The precipitation strengthening increment (49.84MPa) played a dominant role in strength improvement. As partial bainite in the microstructure of 780FB transformed into ferrite at Tp of 800°C, the weakening of phase transformation strengthening (-57.5MPa) became the main factor in strength change. The softening and strength reduction further increased when Tp was up to 980°C, as 780FB completely recrystallized and transformed into ferrite and MA islands. The phase transformation strengthening further reduced by 74.75MPa. When Tp was 1320°C, the VC density decreased from 43/μm2 to 13/μm2, and the (Ti,Nb)C density decreased from 34/μm2 to 14/μm2, leading to severe grain growth (2.24μm to 19.89μm) and bainite transformation. The decrease in precipitation strengthening (-26.86MPa) and fine grain strengthening (-87.91MPa) counteracted with the increase in phase transformation strengthening (51.62MPa), resulting a slight decrease in hardness and strength.

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The evolutions of microstructure and mechanical properties of the welding heat-affected zone in 800MPa-grade complex phase steel

Constitutive Description of Flow Curve for Duplex Titanium Alloy for Hot Forming under Elevated Temperature

Yuki Shimomura, Hyung-Won Park, Hyeon-Woo Park, Yuji Sato, Jun Yanagimoto

Abstract

A novel integrated constitutive equation of the flow curve for Ti–6Al–4V alloys is proposed by incorporating the effects of phase fraction in the hot-forging temperature range. The flow curve was obtained using hot-compression tests in the temperature range of 750–1050 °C and strain rate range of 1–25 s-1. The effects of friction and deformation heat generated during compression were corrected using the inverse analysis method to identify the ideal uniaxial flow curve. The obtained stress parameters were satisfactorily regressed using the rule of mixtures on the α and β phases considering changes in the phase fraction. The integrated flow curve equation incorporating the rule of mixtures of the two phases effectively expressed the flow curve throughout the investigated temperature range. The internal microstructural observation showed that the continuous dynamic recrystallization of the α phase is dominant in the α+β two-phase region, while the deformation of the β phase becomes dominant just below the β transus. The constitutive equation presented here is in good agreement with the temperature dependence of the microstructure.

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Constitutive Description of Flow Curve for Duplex Titanium Alloy for Hot Forming under Elevated Temperature

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