关键词: 智能工艺规划, 五轴数控加工, 图神经网络, 深度强化学习, 多目标寻优, 虚拟仿真

Abstract:

A hierarchical intelligent planning architecture integrating multi-task graph and masked proximal policy optimization (MTG-MPPO) is proposed to address the challenges of factorial expansion of the state space and multi-objective interference in five-axis multi-feature machining. An attributed adjacency graph integrating boundary representation and machining direction vectors is constructed. Probabilistic soft labels are output through a multi-task network to quantify process flexibility and thus achieve prior topological pruning of solution space. An MDP model integrating directed acyclic graph masks and dense reward shaping is built. A Monte Carlo (MC) optimization mechanism based on policy distribution is introduced to coordinate cutting, tool-changing, and rotary-table costs. Simulation results show that MTG-MPPO effectively suppresses disorderly spatial shuttling, reducing the total machining time by 12.33% and 9.81% compared with heuristic baseline algorithms. Under millisecond-level inference, the model escapes local barriers with a maximum probability of 39% to approach better solutions. This validates the computational substitution paradigm that replaces physical machine tool running time with virtual inference, providing a highly robust path for process decision-making in digital twin environments.

Key words: intelligent process planning, five-axis CNC machining, GNN, DRL, multi-objective optimization, virtual simulation