Research on intelligent adaptive NC machining path optimization and multi-constraint scheduling algorithm

Aiming at the problem of dynamic disturbance and multi-constraint coupling in complex workpiece processing in high-end manufacturing, this paper proposes an intelligent adaptive NC machining framework with "path-scheduling-state" third-order cooperation. Firstly, multi-objective constraints such as geometric accuracy, cutting force, thermal deformation, tool wear and equipment load are modeled as markov decision processes (MDP), and a reward function is designed that takes into account accuracy, efficiency and life. Then, a hierarchical optimization architecture is constructed: the high-level uses genetic algorithm (GA) to solve the discrete scheduling problem and realize global task sequencing and resource allocation; The low-level layer uses the depth Q-network (DQN) to optimize the tool path in real time in the continuous motion space, and quickly respond to the changes of working conditions such as material mutation and tool wear. Through the closed-loop interaction of "perception-decision-execution", they realize the dynamic cooperation between routing and scheduling. The experimental results in the high-fidelity simulation environment of aero-engine blades show that compared with the traditional off-line planning and adaptive PID+ rule scheduling, the total processing time of this algorithm is shortened by 11.5%, the comprehensive accuracy deviation is reduced by 34%, the maximum tool wear is reduced by 19.3%, the number of constraint violations is reduced by 93%, and the single decision time is less than 50 ms, which meets the industrial real-time requirements. The research provides a new paradigm of collaborative optimization for intelligent manufacturing, which takes into account global optimization and fast response.