The problems of the strip flatness defects are always severe in the tandem cold rolling process. It is of great significance to predict flatness for flatness control according to the process conditions of products. A prediction model based on convolutional neural network (CNN) was developed in this paper, and it can accurately predict strip flatness under various conditions. According to the distribution characteristics of industrial data collected in the rolling process, the isolated forest algorithm was used to eliminate outliers. Considering the special requirements of CNN on the dimension of input features, the data folding method was used to process the input features. Additionally, since strip flatness data is a vector rather than a scalar, and the length of this vector varies with strip width, which decreases the network's training accuracy. To deal with the problems, the loss function was modified. Taking the Inception module as the basic network structure and inspired by Wide & Deep learning, a strip flatness prediction model with high accuracy was developed. The optimal architecture and parameters of our network were determined through a lot of experimental explorations. The performances of BPNN (Back Propagation Neural Network), DNN (Deep Neural Network), and the proposed model were compared by mean square error (MSE) and coefficient of determination (R²). The result indicates that the proposed model has the highest prediction accuracy and better adaptability. It has the lowest MSE, 0.9891, and the highest R², 0.9555. Finally, the fitting coefficients of Legendre polynomials were used to further prove the excellent prediction performance of the proposed model for strip flatness. Compared with other prediction models, it can obtain the lowest prediction error for the first quadratic and quartic components of strip flatness and it can be well-applied to tandem cold rolling production.

在冷连轧过程中，带钢平整度缺陷问题一直很严重。根据产品的工艺条件预测平整度对平整度控制具有重要意义。本文开发了一种基于卷积神经网络（CNN）的预测模型，它可以准确预测各种条件下的带钢平整度。根据轧制过程中采集的工业数据分布特点，采用孤立森林算法剔除异常值。考虑到CNN对输入特征维度的特殊要求，采用数据折叠的方法对输入特征进行处理。此外，由于条带平坦度数据是向量而不是标量，并且该向量的长度随条带宽度而变化，这降低了网络的' s 训练准确率。为了解决这些问题，修改了损失函数。以Inception模块为基础网络结构，受Wide & Deep学习的启发，开发了一种高精度的带状平坦度预测模型。我们网络的最佳架构和参数是通过大量实验探索确定的。BPNN（反向传播神经网络）、DNN（深度神经网络）和提出的模型的性能通过均方误差（MSE）和决定系数（我们网络的最佳架构和参数是通过大量实验探索确定的。BPNN（反向传播神经网络）、DNN（深度神经网络）和提出的模型的性能通过均方误差（MSE）和决定系数（我们网络的最佳架构和参数是通过大量实验探索确定的。BPNN（反向传播神经网络）、DNN（深度神经网络）和提出的模型的性能通过均方误差（MSE）和决定系数（[R ²）。结果表明，所提出的模型具有最高的预测精度和更好的适应性。它的 MSE 最低，为 0.9891，R ²最高，为 0.9555。最后，利用勒让德多项式的拟合系数进一步证明了所提模型对带钢平整度的良好预测性能。与其他预测模型相比，该方法对带钢平直度的一次二次和四次分量的预测误差最小，可以很好地应用于冷连轧生产中。