4.6 Article

Optimization solution of vertical rolling force using unified yield criterion

Journal

Publisher

SPRINGER LONDON LTD
DOI: 10.1007/s00170-021-08333-3

Keywords

Vertical rolling force; Slip-line method; Dog-bone shape; Unified yield criterion

Funding

  1. National Natural Science Foundation of China [U20A20187]
  2. National Key R&D Program of China [2017YFB0304100]

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This paper presents an innovative method for optimizing the calculation of vertical rolling force, which improves prediction accuracy. By optimizing the weighted coefficient b of intermediate principal shear stress, the model accuracy can be enhanced. The impacts of reduction rate, initial thickness, and friction factor on dog-bone shape size and vertical rolling force are also discussed.
Vertical rolling is an important technique used to control the width of continuous casting slabs in the hot-rolling field. Accurate prediction of vertical rolling force is a core point maintaining rolling-mill equipment. Owing to the limitation of the algorithm in use, the prediction accuracy of most vertical rolling force models based on the energy method can only reach more than 10%. Therefore, it is challenging to optimize the rolling-force model to improve prediction accuracy. An innovative approach for optimizing the calculation of vertical rolling force with a unified yield criterion is presented in this paper. First, the maximal width of a dog-bone region is determined by the slip-line method, and the dog-bone shape is described using a sine-function model. Second, the velocity and corresponding strain-rate fields satisfying kinematically admissible conditions are proposed to calculate the total power of the vertical rolling process. Finally, the analytical solution of the rolling force and the dog-bone-shape model is obtained by repeatedly optimizing the weighted coefficient b of intermediate principal shear stress on the yield criterion. Moreover, the effectiveness of the proposed mechanical model is verified by measured data in the strip hot-rolling field and other models' results. Results show that the prediction accuracy of the vertical rolling force model can be improved by optimizing the value of b. Then, the impacts of reduction rate, initial thickness, and friction factor on dog-bone shape size and vertical rolling force are discussed.

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