Accurately modeling the flow curve over a large strain range is crucial for predicting the flow stress behavior of high silicon steel undergoing strain hardening in the continuous cold rolling process. This study proposes a large strain flow curve model for high-silicon steel, a material commonly used in the cores of electromagnetic devices such as electric motors, generators, and transformers. This model was developed through a series of tensile tests on homogenously pre-strained specimens. Pilot cold rolling was performed at various thickness reduction ratios to impart different magnitudes of pre-strain to sheet-type tensile specimens. The proposed flow curve model was implemented in a VUHARD user-defined subroutine within Abaqus/Explicit, and the predicted roll separating forces were compared with those measured from the pilot cold rolling tests. The comparison demonstrated that the proposed flow curve model accurately captures the flow stress behavior of high-silicon steel at different strain rates over a large strain range, with an R-squared value of 0.9932. The predicted roll separating forces closely matched the measurements from the pilot cold rolling tests, with an average difference of 5.1%.

This study proposes an indirect approach that estimates roll force from motor current signals in a rolling mill. Motor current is first converted to motor torque using an induction-motor equivalent-circuit model, then to roll torque via the gear ratio, and finally to roll force through a torque-arm relationship. A laboratory-scale rolling mill was designed and fabricated to experimentally validate the approach. Two torque-conversion schemes were examined: Method A, which determines the slip of the induction motor from measured rpm and recalculated motor parameters, and Method B, which estimates slip from measured motor current and applies a finite element (FE)–based response surface function to calibrate the converted torque. The converted roll torques were validated against FE analysis, and the resulting roll forces were compared with load cell measurements under various rolling conditions. Deviation, defined as the average difference between the FE-predicted torque and the converted torques, ranged from −11.9% to 28.8% for Method A and −7.2% to 13.8% for Method B. Roll force deviations from measurements ranged from −14.1% to 14.9% for Method A and −3.7% to 14.2% for Method B. Method A provided a straightforward and computationally light conversion route but was more sensitive to rpm-measurement noise, whereas Method B yielded smoother temporal behavior at the cost of FE-based calibration. The results demonstrate that both methods can reproduce the overall evolution of roll torque and roll force using only motor-side measurements, offering a practical foundation for real-time monitoring in rolling mills where stand-by-stand load cells are unavailable.

This study presents a semi-analytical model to accurately predict roll force and setup roll gap (Gset) in the finishing stage of H-beam rolling. The model modifies Sims' classical strip rolling formula to capture the unique geometric and deformation characteristics of H-beam sections. The cross-section is divided into web and flange regions using a rectangular approximation. A geometry-dependent correction function is then introduced to account for the effects of reduction ratio, reduction balance, and contact geometry. This function is calibrated through finite element (FE) simulations under varying rolling conditions. Parameters such as web and flange thickness reduction, junction radius, and flange inclination are systematically adjusted, and the resulting roll forces are used to optimize the function via the Nelder–Mead method.The model is validated using two H-beam specifications from Hyundai Steel. Compared to the company's empirical model, the proposed model reduces web and flange thickness deviations by up to 71%, demonstrating improved prediction accuracy. Statistical analysis also shows substantial reductions in standard deviation and mean error, confirming better dimensional control. This semi-analytical model provides a fast, reliable alternative to full FE simulations and manual adjustment procedures, improving both efficiency and product quality in industrial H-beam rolling.