Prediction of the Stress Decreasing Behavior in the Early Stage of Stress Relaxation in Steel Sheet

Murasawa, Kodai; Ueno, Kota; Kusuda, Yoshinori; Takamura, Masato; Hama, Takayuki; Hakoyama, Tomoyuki; Suzuki, Shinsuke

doi:10.2355/isijinternational.isijint-2021-423

Cited by 4 publications

(2 citation statements)

References 28 publications

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Section: Introductionmentioning

confidence: 99%

“…Additionally, researchers have also tried to use various existing or improved optimisation algorithms applied in various research areas to optimise and improve the performance of learning models. [13][14][15][16][17] The shape control of the cross-section of the steel has the characteristics of high dimension and strong nonlinearity, and it is difficult to directly analyse. A deep neural network (DNN) has a powerful nonlinear fitting ability and can be freely approximated by any nonlinear continuous function.…”

Section: Introductionmentioning

confidence: 99%

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Edge drop prediction of hot-rolled silicon steel based on a mechanism and data model

Wang,

Song,

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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In this article, a high-precision edge drop prediction model for hot-rolled silicon steel based on a rolling mechanism and deep neural network (DNN) is proposed. Considering the initial roll shape, roll wear and roll thermal expansion, a mechanism model of the roll system is established according to the hot rolling 4-high finishing mill. In order to reduce the data dimension and effectively improve the prediction accuracy, the random forest algorithm is employed to analyse the feature parameters that affect the edge drop of hot-rolled silicon steel. Finally, the DNN is introduced to establish an accurate silicon steel edge drop prediction model based on mechanism data and rolling data. The experimental results demonstrated that the proposed DNN model has strong generalisation and reliability with a squared correlation coefficient ( R2) of 0.9656, and it is suitable for application in hot-rolled silicon steel lines for predicting edge drop. Furthermore, the proposed model has a certain reference value for the hot rolling process.

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Section: Introductionmentioning

confidence: 99%