Abstract: Based on measured data of steel plate rolled by single stand 2500 four high reversing mill, the prediction model for front end bending behavior has been established by artificial neural network method. Results showed that artificial neural network calculated value of plate front end bending during rolling conformed to measured value ； as temperature difference between top and bottom surface of rolling work piece was higher, the temperature difference, deformation zone shape characteristic and deformation extent were main factors to effect rolling work piece front end bending. For heavy plate products, decreasing pass reduction could decrease bending, and for thin plate, increasing pass reduction could decrease bending.

摘要： 根据单机架2500四辊可逆式轧机钢板轧制的实测数据,采用人工神经网络方法建立了钢板头部弯曲行为预报模型。结果表明,轧制过程钢板头部弯曲的人工神经网络计算值与实测值符合;当轧件上下表面温度相差较大时,上下表面温差、变形区形状特征和变形程度是影响轧件头部弯曲的主要因素。对于厚的成品板,减小道次压下量可减小弯曲;对薄成品板,增加道次压下量可减小弯曲。