2023
DOI: 10.1002/srin.202300105
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Crown Prediction of Hot‐Rolled Silicon Steel Using Transfer Learning Network Fused with Whale Optimization Algorithm

Abstract: Massive amounts of data are generated during hot strip production in the hot‐rolling metal forming process; the resultant dataset is sufficient for model learning in strip steel crown prediction. However, the data of high‐grade nonoriented silicon steel are limited, and the rolling process parameters differ, resulting in poor crown prediction. Herein, a model based on the whale optimization algorithm and transfer learning to predict the crowns of hot‐rolled high‐grade nonoriented silicon strip steel is present… Show more

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Cited by 6 publications
(1 citation statement)
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“…The factors affecting the strip shape can be divided into three categories: strip properties, rolling equipment and process parameters. 4 In terms of material parameters, steel grades with different carbon content and alloy elements have different deformation resistance, hardness and strength. 5 In rolling process parameters respect, roll deflection caused by rolling force and roll bending force produces crown on the strip steel while roll shifting relieve the roll deflection.…”
Section: Introductionmentioning
confidence: 99%
“…The factors affecting the strip shape can be divided into three categories: strip properties, rolling equipment and process parameters. 4 In terms of material parameters, steel grades with different carbon content and alloy elements have different deformation resistance, hardness and strength. 5 In rolling process parameters respect, roll deflection caused by rolling force and roll bending force produces crown on the strip steel while roll shifting relieve the roll deflection.…”
Section: Introductionmentioning
confidence: 99%