Theoretical investigation reveals critical thickness parameters in thin-strip rolling, improving rolling force predictions and product quality.
The growing demand for high-precision thickness control in cold rolling of metal strips calls for continuous improvement in rolling force prediction models. Classical thin-strip rolling theories, notably the Stone model, are based on the assumption of a circular roll arc profile within the contact deformation zone. In practice, however, this assumption often becomes invalid owing to the emergence of a neutral zone under specific rolling conditions, which introduces considerable errors in force calculation and compromises model applicability. To address this issue, the present study develops a numerical analysis framework for thin-strip rolling processes across a range of initial thicknesses and reductions. The proposed method quantitatively captures the effects of single-pass reduction on roll shape evolution and contact pressure distribution along the deformation arc. In addition, a limiting producible thickness model is proposed by incorporating the deformation efficiency of the strip material. This approach enables the precise delineation of a neutral-zone-free deformation regime and establishes a criterion for determining the optimal single-pass reduction based on the ratio of initial thickness to Stone minimum rolling thickness. For given initial thickness and process parameters, a critical single-pass reduction exists that eliminates the neutral zone, yielding a characteristic curve that divides the rolling regimes: above it, the neutral zone vanishes, the Stone model applies, and efficiency is high; below it, a finite neutral zone persists, partially dissipating rolling force in elastic deformation, though rolling remains viable within limits. These findings provide a robust basis for improving rolling force accuracy and pass optimization, ultimately enhancing product quality and forming efficiency in precision strip manufacturing.
No takes yet. Share an insight, caveat, or question.
Liu et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: