2021
DOI: 10.1115/1.4052703
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Efficient Three-Dimensional Model to Predict Time History of Structural Dynamics in Cold Rolling Mills

Abstract: Introduced is a new physics-based 3D mathematical model capable of efficiently predicting time histories of the nonlinear structural dynamics in cold rolling mills used to manufacture metal strip and sheet. The described model allows for prediction of transient strip thickness profiles, contact force distributions, and roll-stack deformations due to dynamic disturbances. Formulation of the new 3D model is achieved through combination of the highly-efficient simplified-mixed finite element method with a Newmark… Show more

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Cited by 12 publications
(4 citation statements)
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“…Considering first Fig. 5a, the results shown help reveal the transfer characteristics by eliminating the influences of bulk body deformation 1 . This is because at any transverse distance from mill center, the exit thickness profiles in the earlier Fig.…”
Section: Contact Interferencementioning
confidence: 99%
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“…Considering first Fig. 5a, the results shown help reveal the transfer characteristics by eliminating the influences of bulk body deformation 1 . This is because at any transverse distance from mill center, the exit thickness profiles in the earlier Fig.…”
Section: Contact Interferencementioning
confidence: 99%
“…On account of applicability to complex mill configurations (e.g., 20high cluster mills [27]), as well as computational cost and accuracy requirements, the physics-based simplified mixed finite element method (SM-FEM), introduced in [24] and adapted with an advanced contact algorithm to accommodate both low order and high-fidelity roll profiles [20,23,28,29], is adopted herein. Although the SM-FEM model described can be coupled with a Newmark-beta time integration technique to simulate temporal behavior of the 3D roll-stack and predict transient evolution of the rolled strip profile (as described in [1]), a static model is sufficient for the investigations in this work.…”
Section: Strip Profile Prediction Mathematical Formulationmentioning
confidence: 99%
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