Transactions of Materials and Heat Treatment

Title：Rolling force prediction analysis of tandem cold rolling based on proportional loss denoising autoencoder

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ClassificationCode：TG335

year,vol(issue):pagenumber：2022,47(4):190-194

Abstract：

In order to predict the rolling force of tandem cold rolling more accurately, a proportional loss stack denoising autoencoder (SDAE) was designed by the hierarchical extraction and target correlation features. Firstly, the output layer was added to the top layer of SDAE by the stack denoising autoencoder. And then, the weight variables of network were adjusted through some labeled samples. Finally, the depth network variables were adjusted according to the set target parameters, so as to reduce the deviation between network predicted value and target value. Furthermore, by the method of adding target value information in the training process, the effectiveness of feature extraction was improved significantly and had a good predictive stability. The experimental results show that the prediction result of this algorithm is within ±3% error through test, which can meet the actual production control requirements. The algorithm can find the features associated with the target values from the input layer and complete the integration of target values in the pre-training stage. Compared with other prediction algorithms, this algorithm has a small prediction error and can complete convergence faster which shows better prediction accuracy and efficiency.

Funds：

河南省软科学研究计划项目（152400410203）；河南省科技攻关项目（192102210134）

作者简介：张海霞（1980-），女，硕士，讲师 E-mail：zhxhngm@163.com

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