在考虑动态、亚动态再结晶及静态再结晶的基础上，建立了X70管线钢的物理冶金模型，并应用于板带钢热连轧过程奥氏体再结晶、晶粒尺寸和流变应力的预测。结果表明，在合理的温度和压下条件下，应变累积可导致在精轧过程出现动态＋亚动态再结晶行为，促进奥氏体晶粒的进一步细化。终轧温度的降低可引起奥氏体晶粒的粗化和残余应变的显著提高。建立了考虑晶粒尺寸和残余应变影响的平均流变应力（MFS）的人工神经网络预测模型，大大提高了热连轧过程MFS预测精度。
By considering the effect of dynamic, metadynamic and static recrystallization, the physicalmetallurgical model of X70 HSLA steels was developed to predict austenite recrystallization, grain size and flow stress during hotstrip rolling. It was demonstrated that dynamic recrystallization followed by metadynamic recrystallization can occur in the final passes and then austenite grain is further refined under the reasonable rolling schedule. The relatively low finishing temperature can lead to the coarsened grain and high retained strain before transformation. Artificial neural network model, in which the effect of grain size and retained strain is introduced, greatly increases the prediction accuracy of mean flow stress.
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