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International Journal on Interactive Design and Manufacturing (IJIDeM)
Published in: International Journal on Interactive Design and Manufacturing (IJIDeM) 3/2022

11-02-2022 | Original Paper

Online thickness prediction of hot-rolled strip based on ISSA-OSELM

Authors: Sizhu Xiao, Fei Zhang, Xuezhong Huang

Published in: International Journal on Interactive Design and Manufacturing (IJIDeM) | Issue 3/2022

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Abstract

An online sequential extreme learning machine (OSELM) algorithm with self-learning capability based on improved sparrow search algorithm (ISSA) is proposed to solve the problem of feedback lag, strong coupling, and large thickness deviation in thickness prediction in hot continuous rolling process. Firstly, an online thickness prediction model of OSELM is established based on the data collected from hot rolling site. Next, in order to improve the model accuracy and stability, an ISSA method is proposed to optimize the weights and the biases of OSELM to reduce the error between the model and actual production process. On this basis, a self-learning method is applied to further improve the prediction accuracy. Finally, the validity of the ISSA-OSELM with self-learning was verified by simulation. The prediction results can be used to improve the control accuracy of the automatic gauge control (AGC) system, and provide a firm basis for improving the quality of rolled strip products.
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Appendix
Available only for authorised users
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Metadata
Title
Online thickness prediction of hot-rolled strip based on ISSA-OSELM
Authors
Sizhu Xiao
Fei Zhang
Xuezhong Huang
Publication date
11-02-2022
Publisher
Springer Paris
Published in
International Journal on Interactive Design and Manufacturing (IJIDeM) / Issue 3/2022
Print ISSN: 1955-2513
Electronic ISSN: 1955-2505
DOI
https://doi.org/10.1007/s12008-021-00833-6

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