反向凝固过程中铸带厚度及晶粒度的预测

Prediction of the Casting Strip Thickness and Organized Grain during Inverse Solidification Process

基于人工神经网络建立了反向凝固过程中的性能预测模型，实现了对铸带厚度和新相层晶粒度的全面预测；探讨了凝固过程中的主要工艺参数对上述性能的综合影响，为反向凝固性能的综合预测提供了简便的新手段．研究表明，新生相晶粒度随钢水过热度、母带厚度、浸入时间变化对其影响不显著，而钢水过热度、母带厚度、浸入时间变化对铸带厚度的影响较大．该模型的预测结果与实测的结果较为接近．

The artificial natural net can used to predict the property of the strip formed in the molten steel during inverse casting. The property including in casting thickness and new organized grain are comprehensively forecasted. The influences on the property are discussed by the main operated factors during inverse solidification. The new method to predict the property is provided. The new organized grain little changes with molten steel superheat, mother sheet thickness and dip time,but the cast sheet thickness greatly changes with these main operated factors. .The predicted result of the model corresponds to the experienced result.