Dynamics Parameter Calibration for Performance Enhancement of Heavy-Duty Servo Press

The accuracy of dynamics parameters in the transmission system is essential for high-performance motion trajectory planning and stable operation of heavy-duty ser-vo presses. To mitigate the performance degradation and potential overload risks caused by deviations between theoretical and actual parameters, this paper proposes a dynamics model accuracy enhancement method that integrates multi-objective global sensitivity analysis and ant colony optimization-based calibration. First, a nonlinear dynamics model of the eight-bar mechanism was constructed based on Lagrange's equations, which systematically incorporates generalized external force models con-sistent with actual production, including gravity, friction, balance force, and stamping process load. Subsequently, six key sensitive parameters were identified from 28 sys-tem parameters using Sobol global sensitivity analysis, with response functions defined for torque prediction accuracy, transient overload risk, thermal load, and work done. Based on the sensitivity results, a parameter calibration model was formulated to minimize torque prediction error and transient overload risk, and solved by the ant colony algorithm. Experimental validation shows that, after calibration, the root mean square error between predicted and measured torque decreases significantly from 1366.9 N·m to 277.7 N·m (a reduction of 79.7%), the peak error drops by 72.7%, and the servo motor’s effective torque prediction error was reduced from 7.6% to 1.4%. In an automotive door panel stamping application on a 25,000 kN heavy-duty servo press, the production rate increases from 11.4 to 11.6 strokes per minute, demonstrating en-hanced performance without compromising operational safety. This study provides a theoretical foundation and an effective engineering solution for high-precision model-ing and performance optimization of heavy-duty servo presses.