Adaptive threading strategy based on rolling characteristics analysis in hot strip rolling

Wen, Peng; Chen, Shu-zong; Gong, Dunwei; Liu, Zi-ying; Zhang, Dianhua

doi:10.1007/s11771-017-3561-2

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…The rolling spring curve of strip steel is the basis of this study, and the thickness depends on the three main factors of no-load roll gap, rolling mill modulus, and rolling force [34]. According to the bounce curve, the outlet thickness h can be expressed by the following formula.…”

Section: Strip Thickness Influence Weightmentioning

confidence: 99%

“…For the convenience of calculation, the secant method is used to calculate the partial score of the influence coefficient [34]. As shown in Figure 2, take the plastic deformation curve of the plate and strip during rolling as an example.…”

Section: Strip Thickness Influence Weightmentioning

confidence: 99%

“…Order 𝜑 = , The influence coefficient of each variable on the thickness di ence can be obtained, as shown in Table 1. For the convenience of calculation, the secant method is used to calculate the pa score of the influence coefficient [34]. As shown in Figure 2, take the plastic deforma curve of the plate and strip during rolling as an example.…”

Section: ∂P ∂Hmentioning

confidence: 99%

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Quality-Related Process Monitoring and Diagnosis of Hot-Rolled Strip Based on Weighted Statistical Feature KPLS

Guo,

Sun,

Yang

et al. 2023

Sensors

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Rolling is the main process in steel production. There are some problems in the rolling process, such as insufficient ability of abnormal detection and evaluation, low accuracy of process monitoring, and fault diagnosis. To improve the accuracy of quality-related fault diagnosis, this paper proposes a quality-related process monitoring and diagnosis method for hot-rolled strip based on weighted statistical feature KPLS. Firstly, the process-monitoring and diagnosis model of strip thickness and quality based on the KPLS method is introduced. Then, considering that the KPLS diagnosis method ignores the contribution of process variables to quality, it is easy to misjudge the root cause of quality in the diagnosis process. Based on the rolling mechanism model, the influence weight of strip thickness is constructed. By weighing the statistical data features, a quality diagnosis framework of series structure data fusion is constructed. Finally, the method is applied to the 1580 mm hot-rolling process for industrial verification. The verification results show that the proposed method has higher diagnostic accuracy than PLS, KPLS, and other methods. The results show that the diagnostic model based on weighted statistical feature KPLS has a diagnostic accuracy of more than 96% for strip thickness and quality-related faults.

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Section: Strip Thickness Influence Weightmentioning

confidence: 99%

Section: Strip Thickness Influence Weightmentioning

confidence: 99%

Section: ∂P ∂Hmentioning

confidence: 99%

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