Search Results (2)

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. Full article

Machining feature recognition is considered the key connecting technique to the integration of Computer-Aided Design (CAD) and Computer-Aided Process Planning (CAPP), and decision-making of the part processing scheme and the optimization of process route can effectively improve the processing efficiency and reduce the cost of product machining cost. At present, for the recognition of machining features in CAD models, there is a lack of a systematic method to consider process information (such as tolerance and roughness) and an effective process route optimization method to plan part processing procedures. Here we represent a novel model processing feature recognition method, and, on the basis of feature processing plan decision, realize the optimization of the process route. On the basis of a building model Attributed Adjacency Graph (AAG) based on model geometry, topology, and process information, we propose an AAG decomposition and reconstruction method based on Decomposed Base Surface (DBS) and Joint Base Surface (JBS) as well as the recognition of model machining features through Attributed Adjacency Matrix-based (AAM) feature matching. The feature machining scheme decision method based on fuzzy comprehensive evaluation is adopted, and the decision is realized by calculating the comprehensive evaluation index. Finally, the Machining Element Directed Graph (MEDGraph) is established based on the constraint relationship between Machining Elements (MEs). The improved topological sorting algorithm lists the topological sequences of all MEs. The evaluation function is constructed with the processing cost or efficiency as the optimization objective to obtain the optimal process route. Our research provides a new method for model machining feature recognition and process route optimization. Applications of the proposed approach are provided to validate the method by case study. Full article