4.6 Article

Application of convolutional neural networks for prediction of strip flatness in tandem cold rolling process

期刊

JOURNAL OF MANUFACTURING PROCESSES
卷 68, 期 -, 页码 512-522

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jmapro.2021.05.062

关键词

Tandem cold rolling; Strip flatness; Convolutional neural network (CNN); Inception module

资金

  1. Natural Science Foundation of Liaoning Province [2020-MS-094]

向作者/读者索取更多资源

This paper develops a prediction model based on CNN to accurately predict strip flatness under different conditions. Data preprocessing methods like the isolated forest algorithm and data folding technique were utilized. The model achieved high accuracy by modifying the loss function and using an Inception module as the basic network structure.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据