The prediction of the mechanical properties of hot-rolled strips is a very complex, highly dimensional and nonlinear problem, and the published models might lack reliability, practicability and generalization. Thus, a new model was proposed for predicting the mechanical properties of hot-rolled strips by deep learning. First, the one-dimensional numerical data were transformed into two-dimensional data for expressing the complex interaction between the influencing factors. Subsequently, a new convolutional network was proposed to establish the prediction model of tensile strength of hot-rolled strips, and an improved inception module was introduced into this network to abstract features from different scales. Many comparative experiments were carried out to find the optimal network structure and its hyperparameters. Finally, the prediction experiments were carried out on different models to evaluate the performance of the new convolutional network, which includes the stepwise regression, ridge regression, support vector machine, random forest, shallow neural network, Bayesian neural network, deep feed-forward network and improved LeNet-5 convolutional neural network. The results show that the proposed convolutional network has better prediction accuracy of the mechanical properties of hot-rolled strips compared with other models.

热轧带钢力学性能的预测是一个非常复杂的，具有高度尺寸和非线性的问题，已发布的模型可能缺乏可靠性，实用性和通用性。因此，提出了一种通过深度学习预测热轧带钢力学性能的新模型。首先，将一维数值数据转换为二维数据，以表达影响因素之间的复杂相互作用。随后，提出了一个新的卷积网络以建立热轧带材抗拉强度的预测模型，并向该网络中引入了改进的起始模块，以从不同尺度提取特征。为了找到最佳的网络结构及其超参数，进行了许多比较实验。最后，在不同模型上进行了预测实验，以评估新卷积网络的性能，其中包括逐步回归，岭回归，支持向量机，随机森林，浅层神经网络，贝叶斯神经网络，深层前馈网络和改进的LeNet-5卷积神经网络。结果表明，与其他模型相比，所提出的卷积网络具有更好的热轧带钢力学性能预测精度。