Kinetics Model of Bainitic Transformation With Stress

Mingxing Zhou,Guang Xu,Haijiang Hu,Qing Yuan,Junyu Tian 대한금속·재료학회 2018 METALS AND MATERIALS International Vol.24 No.1

Thermal simulations were conducted on a Gleeble 3800 simulator. The main purpose is to investigate the effects ofstress on the kinetics of bainitic transformation in a Fe-C-Mn-Si advanced high strength bainitic steel. Previousstudies on modeling the kinetics of stress affected bainitic transformation only considered the stress below theyield strength of prior austenite. In the present study, the stress above the yield strength of prior austenite is takeninto account. A new kinetics model of bainitic transformation dependent on the stress (including the stresses belowand above the yield strength of prior austenite) and the transformation temperature is proposed. The new modelpresents a good agreement with experimental results. In addition, it is found that the acceleration degree of stresson bainitic transformation increases with the stress whether its magnitude is below or above the yield strength ofaustenite, but the increasing rate gradually slows down when the stress is above the yield strength of austenite.

Combined Effect of the Prior Deformation and Applied Stress on the Bainite Transformation

Mingxing Zhou,Guang Xu,Li Wang,Haijiang Hu 대한금속·재료학회 2016 METALS AND MATERIALS International Vol.22 No.6

There has been a continued interest over the past years in the effects of external stress or prior deformation onthe bainite transformation. In this study, the combined effect of prior deformation and stress on the bainitetransformation was investigated and the interaction between the effects of prior deformation and stress wasdiscussed in detail. The results show that although single deformation and single stress promote the bainitetransformation, their combination cannot promote the bainite transformation to a much larger degree. In addition, atthe early stage of transformation, the promotion effects of prior deformation and stress on the amount of the bainitetransformation are enhanced by each other. However, at the latter stage, the deformation weakens the promotioneffect of the stress. Moreover, prior deformation at a low temperature accelerates the kinetics of the bainite transformationwith stress, but it decreases the amount of the bainite transformation even if the deformation is small.

Comprehensive Compensation Method for Harmonics and Negative Sequence Current in High-speed Railway under Grid Voltage Distortion

Mingxing Tian,Yuqi Zhou,Hong Yan 보안공학연구지원센터 2014 International Journal of Control and Automation Vol.7 No.7

STUDIES ON SYNTHESIS OF METHYL GLYCOLATE AND METHYL METHOXY ACETATE FROM THE COUPLING OF FORMALDEHYDE AND METHYL FORMATE

He, Dehua,Huang, Weiguo,Liu, Jinyao,Zhou, Mingxing,Zhu, Qiming 한국화학공학회 1998 Korean Journal of Chemical Engineering Vol.15 No.5

Catalytic performance of various acids in the coupling reaction of formaldehyde and methyl formate to produce methyl glycolate and methyl methoxy acetate has been studied. The influence of reaction conditions, such as catalyst amount, reaction temperature, reaction time, and molar ratio of formaldehyde to methyl formate, has also been investigated. The results showed that the acid strength had great influence on the reaction, namely, stronger acds had higher activities. It was also found that the reaction temperature and time had significant effect on the reaction, and the preferable conditions were quite different as different acids were used.

Effect of Ni Addition on Bainite Transformation and Properties in a 2000 MPa Grade Ultrahigh Strength Bainitic Steel

Junyu Tian,Guang Xu,Zhengyi Jiang,Haijiang Hu,Mingxing Zhou 대한금속·재료학회 2018 METALS AND MATERIALS International Vol.24 No.6

The effects of Nickle (Ni) addition on bainitic transformation and property of ultrahigh strength bainitic steels are investigatedby three austempering processes. The results indicate that Ni addition hinders the isothermal bainite transformation kinetics,and decreases the volume fraction of bainite due to the decrease of chemical driving force for nucleation and growth ofbainite transformation. Moreover, the product of tensile strength and total elongation (PSE) of high carbon bainitic steelsdecreases with Ni addition at higher austempering temperatures (220 and 250 °C), while it shows no significant differenceat lower austempering temperature (200 °C). For the same steel (Ni-free or Ni-added steel), the amounts of bainite and RAfirstly increase and then decrease with the increase of the austempering temperature, resulting in the highest PSE in thesample austempered at temperature of 220 °C. In addition, the effects of austempering time on bainite amount and propertyof high carbon bainitic steels are also analyzed. It indicates that in a given transformation time range of 30 h, more volumeof bainite and better mechanical property in high carbon bainitic steels can be obtained by increasing the isothermal transformationtime.

Microstructure and Properties of a Low Carbon Bainitic Steel Produced by Conventional and Inverted Two-Step Austempering Processes

Junyu Tian,Wei Wang,Guang Xu,Xiang Wang,Mingxing Zhou,Hatem Zurob 대한금속·재료학회 2023 METALS AND MATERIALS International Vol.29 No.5

The microstructure evolution and strain hardening behaviour of a low-carbon carbide-free bainitic steel prepared by eithersingle-step austempering (420 °C or 365 °C), conventional two-step austempering (420 °C then 365 °C) or inverted two-stepaustempering (365 °C then 420 °C) treatments were investigated. The results show that when the total isothermal holdingtime was the same, the inverted two-step austempering treatment (first completing bainitic transformation at low-temperatureand then annealing at high-temperature austempering) led to the highest toughness (30.7 GPa%) due to the finer bainiticmicrostructure and higher fraction of film-like retained austenite. Grain refinement and transformation-induced plasticityallowed the material to achieve high ductility without sacrificing strength. Comparing single-step austempering at 365 °Cwith the inverted two-step austempering process indicates that annealing at a higher temperature after completion of thebainitic transformation resulted in better tensile properties because of a lower dislocation density and more stabler retainedaustenite. In addition, the samples heat-treated by the conventional two-step austempering process exhibited slower bainitetransformation kinetics and the worse tensile properties than the sample which was heat-treated using a single-step austemperingtreatment at 365 °C or the one which was heat-treated using an inverted two-step heat treatment. Through the analysisof the orientation relationships, it is observed that the original austenite and the bainitic plates mainly followed the K-Sorientation relationships regardless of whether the bainite plates were formed in the first or the second heat-treatment step.

Prediction Model for unfavorable Outcome in Spontaneous Intracerebral Hemorrhage Based on Machine Learning

Shengli Li,Jianan Zhang,Xiaoqun Hou,Yongyi Wang,Tong Li,Zhiming Xu,Feng Chen,Yong Zhou,Weimin Wang,Mingxing Liu 대한신경외과학회 2024 Journal of Korean neurosurgical society Vol.67 No.1

Objective : The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). Methods : Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). Results : We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables. Conclusion : The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.