4.6 Article

Investigation of the initial residual stress effects on a work roll maximum in-service stress in hot rolling process by a semi-analytical method

期刊

JOURNAL OF MANUFACTURING PROCESSES
卷 99, 期 -, 页码 53-64

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ELSEVIER SCI LTD
DOI: 10.1016/j.jmapro.2023.04.084

关键词

Residual stress; von Mises stress; Rolling; Artificial neural network; Genetic algorithm

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A detailed finite element model was developed to estimate the thermo-mechanical stress and residual stress components of an industrial hot rolling work roll. A semi-analytical procedure was also developed to study the effect of initial residual stress on maximum von Mises stress. Residual stress components were measured using the ring-core method and compared with the estimated values to validate the finite element model. Artificial neural network and genetic algorithm were used to predict the maximum von Mises stress and optimize the initial residual stress components to minimize stress at the work roll surface where fatigue cracks initiate and propagate.
A High magnitude of von Mises stress is considered a source of plastic deformation, fatigue crack initiation, and surface defects. A Detailed finite element model (FEM) was proposed here to estimate the thermo-mechanical stress and residual stress components at an industrial hot rolling work roll during the rolling process. Also, a semi-analytical procedure was developed here to study the effect of the initial residual stress components that remained after the work roll heat treatment process on maximum von Mises stress. The work roll referred to here was an Indefinite Chilled Double Poured (ICDP) cast iron roll that was used as the fifth finishing stand of strips hot rolling. Residual stress components were measured by the ring-core method at two stages, after the heat treatment process as initial components and after the service life of the work roll as final components. Estimated residual stress components by the developed method were compared with final experimental residual stress measurements to verify the FEM. Finally, by using artificial neural network and genetic algorithm, the maximum value of von Mises stress was predicted, and initial residual stress components were optimized in order to minimize the maximum value of attained von Mises stress at the work roll surface, where the most fatigue cracks initiate and grow.

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