基于深度神经网络的斜轧穿孔机调整参数预测

Prediction on adjusting parameters for skew rolling puncher based on deep neural network

针对无缝钢管二辊斜轧穿孔生产工艺中轧机调整参数对钢管质量影响较大,且传统机理公式计算的设定值精度不高的问题,提出了基于深度神经网络的斜轧穿孔机调整参数预测模型。首先,综合分析了传统的调整参数的数学模型,并在此基础上确定了调整参数的主要影响因素。依据现场收集的数据集,训练了二辊斜轧穿孔时轧机参数的深度神经网络预测模型。在训练深度神经网络时,运用小批量梯度下降法和Adam算法相结合的方法进行了梯度估计修正,优化了训练速度。仿真结果表明:经深度神经网络模型预测的轧机调整参数与实测数据比较,预测模型的R-squared值控制在0.98左右,调整参数的相对误差均可控制在5%以内。相比于传统数学模型,该预测模型具有更高的预测精度,能够实现轧机调整参数高精度预测并用于指导生产。

Aiming at the problems that the adjustment parameters of rolling mill has a great influence on the quality of steel pipe in the two-roll skew rolling and punching production process of seamless steel pipes,and the accuracy of the set value calculated by the traditional mechanism formula is not high,a prediction model for adjustment parameters of skew rolling puncher based on deep neural network was proposed.Firstly,the traditional mathematical model of adjustment parameters was analyzed comprehensively,and the main influencing factors were determined on this basis.Then,based on the data set collected in the field,the deep neural network prediction model of rolling mill parameters during the two-roll skew rolling and punching was trained,and in the deep neural network training,the gradient estimation corection was conducted by using the combination of mini-batch gradient descent method and Adam algorithm to optimize the training speed.The simulation results show that the adjustment parameters of rolling mill predicted by the deep neural network model are compared with the measured data,the R-squared value of the prediction model is controlled at about O.98,and the relative error of the adjustment parameters can be controlled within 5%.Compared with the traditional mathematical model,this prediction model has higher prediction accuracy,and can realize the high-precision prediction of rolling mill adjustment parameters and be used to guide production.