An Improved VAE-Based Quality Prediction Method with Application to a Hot Strip Rolling Mill Process DOI

Yundan Liu,

Kai Zhang, Xiaowen Zhang

et al.

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS), Journal Year: 2024, Volume and Issue: unknown, P. 783 - 788

Published: May 17, 2024

Language: Английский

IoT-based framework for digital twins in steel production: A case study of key parameter prediction and optimization for CSR DOI
Jingdong Li,

Xiaochen Wang,

Quan Yang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 250, P. 123909 - 123909

Published: April 4, 2024

Language: Английский

Citations

11
基于混合多阶集成模型的非平衡热轧带钢凸度智能诊断 DOI

Chengyan Ding,

Jie Sun, Xiaojian Li

et al.

Journal of Central South University, Journal Year: 2024, Volume and Issue: 31(3), P. 762 - 782

Published: March 1, 2024

Language: Английский

Citations

5
An interpretable framework for high-precision flatness prediction in strip cold rolling DOI

Chengyan Ding,

Jun-Cheng Ye,

Jia-Wei Lei

et al.

Journal of Materials Processing Technology, Journal Year: 2024, Volume and Issue: 329, P. 118452 - 118452

Published: May 24, 2024

Language: Английский

Citations

5
A novel deep ensemble reinforcement learning based control method for strip flatness in cold rolling steel industry DOI
Wen Peng,

Jiawei Lei,

Chengyan Ding

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 134, P. 108695 - 108695

Published: May 31, 2024

Language: Английский

Citations

4
Novel shape control system of hot-rolled strip based on machine learning fused mechanism model DOI

LingMing Meng,

Jingguo Ding, Xiaojian Li

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124789 - 124789

Published: July 14, 2024

Language: Английский

Citations

4
Predictive modeling of strip width based on incremental learning and adaptive-weight fusion during the hot rolling process DOI

Wenteng Wu,

Wen Peng, Yü Liu

et al.

Journal of Manufacturing Processes, Journal Year: 2025, Volume and Issue: 142, P. 157 - 176

Published: April 1, 2025

Language: Английский

Citations

0
Modeling and Simulation of Shape Control Based on Digital Twin Technology in Hot Strip Rolling DOI Creative Commons
Youzhao Sun, Jingdong Li,

Yamin Sun

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(2), P. 614 - 614

Published: Jan. 18, 2024

Focusing on the problem of strip shape quality control in finishing process hot rolling, a model based metal flow and stress release with application varying contact rolling parameters is introduced. Combined digital twin technology, framework proposed, which realizes deep integration between physical time–space virtual time–space. With utilization historical data, are optimized iteratively to complete model. According schedule, raw material information taken as input obtain simulation shape, shows variety export conditions. The prediction absolute error crown flatness less than 5 μm I-unit, respectively. results prove that proposed strong performance can be effectively applied production. In addition, provides operators reference for parameter settings actual production promotes intelligent strategy.

Language: Английский

Citations

3
Application of digital twin for industrial process control: A case study of gauge-looper-tension optimized control in strip hot rolling DOI Creative Commons
Jie Sun, Chen Shang,

Chengyan Ding

et al.

Digital Twin, Journal Year: 2025, Volume and Issue: 4, P. 10 - 10

Published: Feb. 7, 2025

During the hot rolling process, performance of most control systems gradually degrades due to equipment aging and changing process conditions. However, existing gauge-looper-tension method remain restricted by a lack intelligent parameter maintenance strategies. To address this challenge enhance smart manufacturing capabilities strip rolling, based on digital twin method, paper proposes data-driven optimized for system rolling. First, model is constructed serve as an evaluation optimization platform. Subsequently, relevant data are collected calculate Hurst index identifying controller during process. Additionally, controllers with poor values, crayfish algorithm employed adjusting parameters maximize index. Experimental results demonstrate that effectively recognized state successfully enhances system. Therefore, platform, proposed can maintain performance-degraded systems.

Language: Английский

Citations

0
Explainable machine learning for predicting mechanical properties of hot-rolled steel pipe DOI
Jingdong Li, Youzhao Sun, Xiaochen Wang

et al.

Journal of Iron and Steel Research International, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Language: Английский

Citations

0
Two-stream neural network for the prediction of multiple defects in continuous casting DOI
Xin-Yu Ning, Haijun Li, Qibo Wang

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113116 - 113116

Published: April 1, 2025

Language: Английский

Citations

0