Parameter Estimation for Online Calculation Model of Hot-rolled Strip Crown

Online crown calculations are essential for effective shape control of hot-rolled strip steel, the accuracy of which is often limited by modeling assumptions and the complexity of operating conditions. Model parameters significantly influence calculation accuracy, requiring process engineers to make regular adjustments based on their experience. As customization, small batch sizes, and multiple specifications become mainstream in production and supply modes, reliance on manual experience is increasingly insufficient. This paper aims to enhance the accuracy of online crown calculations for hot-rolled strip steel. Initially, based on the mechanism model incorporating gain coefficients and formula derivation, a relational expression between the gain coefficients for each stand in the tandem rolling mill group and the final crown is established, transforming the discrete issue of correcting influential factors into a unified problem of solving gain coefficients, thus improving correction efficiency. To ensure the reliability of the estimated results, three forms of constructing the constraint equation are compared: no constraint, limiting the proportional crown difference, and proportional exit crown. Utilizing rolling production data, two parameter estimation methods—normal linear regression and robust regression—are explored to determine the gain coefficients. Results indicate that combining the constraint based on proportional exit crown and robust regression will provide more reliable gain coefficients and significantly enhances the accuracy of online crown calculations, improving precision by over 50% compared with the model before correction.

Authors

Institutions

Publication Details

Journal
ISIJ International
Published
2025-06-11
DOI
https://doi.org/10.2355/isijinternational.isijint-2024-371
Primary Topic
Surface Roughness and Optical Measurements
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Parameter Estimation for Online Calculation Model of Hot-rolled Strip Crown

Chihuan Yao, Changke Chen, Qingxiao Feng, Chao Liu et al.
ISIJ International
Surface Roughness and Optical Measurements
article

Parameter Estimation for Online Calculation Model of Hot-rolled Strip Crown

Chihuan Yao, Changke Chen, Qingxiao Feng, Chao Liu, Anrui He, H. L. Qu
article en

Abstract

Online crown calculations are essential for effective shape control of hot-rolled strip steel, the accuracy of which is often limited by modeling assumptions and the complexity of operating conditions. Model parameters significantly influence calculation accuracy, requiring process engineers to make regular adjustments based on their experience. As customization, small batch sizes, and multiple specifications become mainstream in production and supply modes, reliance on manual experience is increasingly insufficient. This paper aims to enhance the accuracy of online crown calculations for hot-rolled strip steel. Initially, based on the mechanism model incorporating gain coefficients and formula derivation, a relational expression between the gain coefficients for each stand in the tandem rolling mill group and the final crown is established, transforming the discrete issue of correcting influential factors into a unified problem of solving gain coefficients, thus improving correction efficiency. To ensure the reliability of the estimated results, three forms of constructing the constraint equation are compared: no constraint, limiting the proportional crown difference, and proportional exit crown. Utilizing rolling production data, two parameter estimation methods—normal linear regression and robust regression—are explored to determine the gain coefficients. Results indicate that combining the constraint based on proportional exit crown and robust regression will provide more reliable gain coefficients and significantly enhances the accuracy of online crown calculations, improving precision by over 50% compared with the model before correction.

ISIJ InternationalVol. 65(9)
University of Science and Technology of Benin (BJ), Institute of Research of Iron and Steel Shasteel (CN), University of Science and Technology Beijing (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 86%
Surface Roughness and Optical Measurements
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.