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| Dimension reduction method of cold rolling strip flatness data based on autoencoder |
| XU Yang-huan1, WANG Dong-cheng1,2, WANG Yong-mei1, YUAN Wen-yue3, YU Hua-xin1,2, LIU Hong-min1,2 |
1. National Engineering Research Center for Equipment and Technology of Cold Rolling Strip, Yanshan University, Qinhuangdao 066004, Hebei, China;
2. State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao 066004, Hebei, China;
3. Northeastern University at Qinhuangdao, Qinhuangdao 066004, Hebei, China |
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Abstract In order to realize the intelligent manufacturing of plate and strip rolling process, it is necessary to deeply explore the connotation of intelligent manufacturing. For specific problems, it is of great significance to apply the unsupervised learning and the reinforcement learning theory to production practice. The flatness detection data in the process of strip rolling is taken as the research object and the autoencoder in the unsupervised learning theory is used to automatically learn the basic flatness mode, so as to reduce the amount of storage and transmission of flatness data, realize the abstract representation of flatness distribution, and lay the foundation for the flatness anomaly detection, the intelligent prediction and the intelligent control. Compared with the traditional flatness data dimension reduction method based on Legendre polynomial, the accuracy of flatness reconstruction can be significantly improved and the approximate lossless compression of the flatness data can be realized applying the present method.
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Received: 06 May 2021
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2021, Vol. 56 
