An Intelligent Process Planning Method for Shaft Parts Based on Multi-Graph Fusion: From Feature Recognition to Process Route Generation

Conventional process planning methods—typically driven by rules or expert heuristics—struggle to handle complex structural components, often yielding low efficiency and limited optimization. This paper proposes an intelligent process planning method for shaft parts based on multi-graph fusion. The framework integrates three core stages—machining feature recognition, machining scheme decision-making, and process route planning—into an end-to-end pipeline that transforms 3D models directly into executable machining routes. First, machining features are automatically identified using an attributed adjacency graph (AAG) representation coupled with a graph attention network (GAT). Next, the system leverages process knowledge and machining parameters to assign the optimal processing scheme for each feature. Finally, a sequence prediction model built on a machining-element directed graph (MEDGraph) generates process routes that satisfy manufacturing constraints. Experimental results demonstrate recognition and planning accuracies of 98.97% and 96.14%, respectively, underscoring the robustness and effectiveness of the proposed framework. This work establishes a unified pathway from design geometry to process execution, offering a powerful enabler for intelligent and adaptive manufacturing.