Dynamic optimization and precision evolution model of complex mechanical system in digital manufacturing environment

In digital manufacturing, complex mechanical systems are faced with the problems of performance attenuation and precision control in dynamic uncertain environment. This paper proposes a dual-mode driving framework of dynamic optimization-precision evolution. The concept of "dynamic precision field" is innovatively introduced into the framework, and the coupling effects of temperature, stress, vibration and other physical fields are integrated by partial differential equations to realize millisecond real-time prediction of the temporal and spatial evolution of machining accuracy. A dynamic optimization model based on model predictive control (MPC) is constructed, and the hybrid enhanced intelligent algorithm combined with GPU parallel acceleration is used to achieve 15ms super real-time solution under 500,000-dimensional variables. At the same time, the SHAP interpreter and decision tree rule base are integrated to form an interpretable industrial AI decision system. In the practice of machining aerospace turbine blades, the framework reduces the average profile error from 9.5μm to 3.2μm by more than 65%, and reaches the accuracy of IT5 level stably, and completes the adaptive adjustment within 50ms under abrupt working conditions. The research verifies the remarkable advantages of the proposed model in improving accuracy consistency, real-time calculation and environmental robustness, and provides an intelligent solution for digital manufacturing.