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以0.05～0.33C(wt%,下同)、0.004～0.099V的3种微合金钢分别在1000和1050℃、0.01～10s-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。
Three kinds of microalloyed steels with 0.05-0.33C (wt%) and 0.004-0.099V were respectively tested with Gleeble-1500 heat / force at 1000 and 1050 ℃ and 0.01-10s-1 strain rate to simulate the experimental stress-strain (BP) artificial neural network model of micro-alloyed steel with different contents of C and V on the peak strain εp of dynamic recrystallization was constructed. The BP model was used to study the effects of C , V content on the impact of εp law. The results show that the influence of C and V on the peak strain of dynamic recrystallization in V-containing microalloyed steels is related to the strain rate, and the effect of the elements under high strain rate and low strain rate is different.