C、V含量对微合金钢变形奥氏体动态再结晶峰值应变的影响
Influence of C, V Content on Peak Strain of Deformed Austenite in Micro-alloyed Steel
吴晋彬1, 刘国权1,2, 王承阳1, 许 磊1
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作者单位：1. 北京科技大学 材料科学与工程学院, 北京 100083; 2. 北京科技大学 新金属材料国家重点实验室, 北京 100083
中文关键字：变形奥氏体； 峰值应变； 神经网络
英文关键字：deformed austenite; peak strain; artificial neural network
中文摘要：以0.05～0.33C(wt%，下同)、0.004 ～0.099 V的3种微合金钢分别在1000和1050 ℃、0.01～10 s-1应变速率下以Gleeble-1500热/力模拟实验应力-应变数据为样本，构建了C、V含量不同的微合金钢成分对动态再结晶峰值应变εp影响的误差反向传播(BP)人工神经网络模型，利用建立的BP模型研究了在不同应变速率下C、V含量对εp的影响规律。研究结果表明， C、V对含钒微合金钢动态再结晶峰值应变的影响与应变速率相关，高应变速率和低应变速率下元素的影响规律不同。
英文摘要：The models of error back propagation (BP) neural network, which was the relationship between the content (C, V) and peak strain (εp ), were established on the basis of the data in Gleeble-1500 thermo-mechanical simulated experiment on the condition of isothermal compression at 1000 ℃, 1050 ℃ and strain rate range of 0.01～10 s-1 for 3 micro-alloyed steel with 0.05wt%C～0.33wt%C, 0.004wt%～0.099wt%V. The effects of C, V content on εp of vanadium micro-alloyed steel were researched using BP neural network models. The results show that the influences of C, V content on εp are related to strain rate. The influence law at high strain rate is different from that at low strain rate.