Intelligent Control for Dynamic Errors of Aspherical Grinding Machines Via Digital Twin

Aiming at the core problem that time-varying dynamic errors caused by the evolution of machine tool dynamic characteristics during aspherical grinding are hard to predict in real time and compensate actively, this study proposes an integrated digital twin system framework of “mechanism modeling-virtual-real fusion-intelligent control”. A high-fidelity virtual grinding machine integrating geometric, physical and behavioral models is constructed, with real-time virtual-real data synchronization realized via a multi-source sensor network. Based on finite element and experimental modal analysis, key structural natural frequencies are obtained, and a resonance risk early warning model is established using the rotation speed-order mapping relationship. An innovative intelligent control strategy based on PPO-based DRL is designed, which adaptively optimizes process parameters (e.g., spindle speed, feed rate) according to dynamic error prediction trends to achieve resonance avoidance and vibration suppression. This system provides a new “prediction-control” collaborative accuracy guarantee paradigm for high-precision aspherical grinding.