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首页> 外文期刊>Indian journal of engineering and materials sciences >Modeling of austenitic grain growth of 25CrMo4 steel for the high-speed railway axle during hot working
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Modeling of austenitic grain growth of 25CrMo4 steel for the high-speed railway axle during hot working

机译:高速铁路车轴热加工过程中25CrMo4钢奥氏体晶粒长大的模型

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摘要

After hot deformation, the fine grains due to recrystallization are apt to grow up at high temperatures. The grain size affects directly the performance and quality of products, so it is of great significance to investigate the grain evolution. In this paper, 25CrMo4 steel samples are compressed until a strain of 0.6, under isothermal conditions using Gleeble-1500 at deformation temperatures in the range of 950-1100°C and at same strain rate 1.0 s"1, and then the samples are held at deformation temperatures for 0, 10, 20 and 30 min. Microstructure is retained by using water quench. The grain growth model is expressed by a differential function of both temperature and holding time. The material constants in grain growth model are determined using genetic algorithm (GA) optimization technology from experimental data. A good agreement between predicted results and experimental data is obtained, which shows that the developed grain growth model enables the grain size evolution at various high temperatures to be well predicted.
机译:热变形后,由于重结晶引起的细晶粒易于在高温下长大。晶粒尺寸直接影响产品的性能和质量,因此研究晶粒的演变具有重要意义。在本文中,将25CrMo4钢样品在等温条件下使用Gleeble-1500在950-1100°C的变形温度和1.0 s“ 1的相同应变速率下压缩至0.6应变,然后将其固定在0、10、20和30分钟的变形温度下,通过水淬保持微观结构,用温度和保温时间的微分函数表示晶粒生长模型,并使用遗传算法确定晶粒生长模型中的材料常数实验数据优化(GA)技术,预测结果与实验数据吻合良好,表明所建立的晶粒长大模型可以较好地预测各种高温下的晶粒尺寸演化。

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