Flatness pattern recognition based on stacked sparse denoising autoencoder and improved Osprey optimisation algorithm kernel-extreme learning machine

Aiming at the problems of random noise and insufficient extraction of flatness features in the data detected by the plate shaper during the rolling process of cold-rolled strip steel, this paper proposes a flatness recognition method based on stack sparse denoising autoencoder (SSDAE) with improved...

Full description

Saved in:

Bibliographic Details
Published in:	Ironmaking & steelmaking
Main Authors:	Zhou, Yaluo, Zhang, Shaochuan, Liu, Wenguang, Zhang, Ruicheng
Format:	Journal Article
Language:	English
Published:	11.07.2025
ISSN:	0301-9233, 1743-2812
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Aiming at the problems of random noise and insufficient extraction of flatness features in the data detected by the plate shaper during the rolling process of cold-rolled strip steel, this paper proposes a flatness recognition method based on stack sparse denoising autoencoder (SSDAE) with improved Osprey optimisation algorithm kernel-extreme learning machine (IOOA-KELM). The method first uses SSDAE to denoise and downscale the flatness data to achieve efficient feature extraction. Then, the regularisation coefficients and kernel parameters of KELM are optimised using IOOA to construct an accurate and efficient flatness recognition model. Verified by MATLAB simulation, the established model performs well in cold-rolled strip flatness recognition, with the mean value of root mean square error (RMSE) reduced to 0.0011, which improves the recognition performance by about 95% compared with that of traditional feature extraction methods. The experimental results show that the method has strong anti-interference ability, fast convergence speed and high recognition accuracy, which is a practical and efficient flatness pattern recognition method suitable for the quality needs in modern steel production.
ISSN:	0301-9233 1743-2812
DOI:	10.1177/03019233251324775