改进的神经网络在连铸板坯缺陷预报中的应用

Application of Improvement Neural Network to Defect Prediction of Continuous Cast Slab

在传统BP神经网络算法的基础上提出了一些改进措施,如采用了变步长的学习方法、加入了动量项,以防止网络振荡,达到了加速网络收敛的效果.本研究分析了表面纵裂成因及影响因素,以梅钢生产的焊瓶钢HP295为例构建表面纵裂预报系统,利用改进的BP网络预报表面纵裂,通过系统的分析发现焊瓶钢HP295表面纵裂产生的原因主要是二冷水分配不均匀.因此实际生产中,通过调节二冷水比例减少表面纵裂的产生.

The conventional back-propagation for neural network is improved by introducing the variable-step learning rate with a momentum term added in so as to prevent the network from error surge and accelerate its convergence rate.Then,the causes and influencing factors on the longitudinal cracks on slab surface in the continuous casting process are analyzed,and a prediction system of longitudinal surface cracks of slab is set up with the HP295 steel supplied by Meishan Steelworks as example,based on the improved BP neural network.It is found that the root cause of the longitudinal surface cracks on HP295 is the nonuniform distribution of the secondary cooling water.So,adjusting the proportion of the secondary cooling water is the efficient way to reduce the formation of the longitudinal surface cracks in practical production.