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作 者:刘杨 李志强[3] 赵冰 杨涛 孙朝阳[1,2,4] Liu Yang;Li Zhiqiang;Zhao Bing;Yang Tao;Sun Chaoyang(School of Mechanical Engineering,University of Science and Technology Beijing,Beijing 100083,China;Beijing Key Laboratory of Lightweight Metal Forming,University of Science and Technology Beijing,Beijing 100083,China;AVIC Manufacturing Technology Institute,Beijing 100024,China;Shunde Graduate School,University of Science and Technology Beijing,Foshan 528000,China)
机构地区:[1]北京科技大学机械工程学院,北京100083 [2]北京科技大学金属轻量化成形制造北京市重点实验室,北京100083 [3]中国航空制造技术研究院,北京100024 [4]北京科技大学顺德研究生院,广东佛山528000
出 处:《稀有金属材料与工程》2022年第10期3752-3761,共10页Rare Metal Materials and Engineering
基 金:中央高校基本科研业务费(FRF-BD-20-08A,FRF-TP-20-009A2);北京科技大学顺德研究生院科技创新专项资金(BK19CE008)。
摘 要:通过单向拉伸实验研究了TA32钛合金在温度880~940℃、初始应变速率5×10^(-4)~1×10^(-2)s^(-1)条件下的超塑性变形行为和微观组织演变,构建了修正Johnson-Cook本构模型和BP神经网络本构模型。结果表明,TA32钛合金的流动应力与断后伸长率对温度和应变速率敏感,应变速率敏感性指数随应变量增加而减小,随温度升高而增大。温度升高和变形程度增大促进了α→β相转变和两相晶粒长大,应变速率降低使得两相晶粒有所长大。β相晶粒形貌随变形条件改变有显著变化,α相晶粒则保持等轴状。TA32钛合金的超塑性断裂模式为孔洞生长扩展导致的微孔聚集性断裂。相较于修正Johnson-Cook本构模型,BP神经网络本构模型在大范围变形条件下的预测精度更高。The mechanical behavior and microstructure evolution of TA32 titanium alloy during superplastic deformation were investigated by uniaxial tensile tests at different temperatures(880~940°C)and initial strain rates(5×10^(-4)~1×10^(-2)s^(-1)).The modified Johnson-Cook constitutive model and BP neural network constitutive model were established.The results indicate that the flow stress and elongation of TA32 titanium alloy are sensitive to temperature and strain rate.The strain rate sensitivity exponent decreases with the increase of strain and increases with the increase of temperature.Increase of temperature and deformation degree promotes theαtoβphase transformation and the grain growth of the two phases.Decrease of strain rate moderately increases the grain size of the two phases.The morphology ofβphase grains changes significantly with the change of deformation conditions,whileαphase grains remains equiaxed.The superplastic fracture mode of TA32 titanium alloy is microporous aggregation fracture caused by the growth and coalesce of cavities.The prediction accuracy of BP neural network constitutive model is higher than that of modified Johnson-Cook constitutive model under large scale deformation condition.
分 类 号:TG146.23[一般工业技术—材料科学与工程] TG115.21[金属学及工艺—金属材料]
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