Feature points classification of computerized numerical control finishing tool path based on graph neural network

Abstract

In Computer Numerical Control (CNC) machining, surface defects occur due to inaccurate recognition of feature points in the finishing tool path and unsmooth feed rate planning by the CNC system. Therefore, accurately identifying feature points of tool path is crucial for high-speed and high-precision CNC machining. Existing algorithms often rely on simple, manually set thresholds and do not consider cross directional geometric information of tool path, leading to poor performance in feature point recognition. This paper presents a new method for identifying and classifying feature points using a graph neural network (GNN) by aggregating geometric features from both the feed and cross directions of the tool path to automatically and accurately identify feature points in finishing tool paths. The method begins by creating a graph-based representation of the tool path, which provides detailed geometric information for Cutter Location (CL) points. It also introduces an algorithm for identifying cross directional points related to CL points and a spatial convolution method that combines feed and cross directional geometric features, based on which a Feature Point-Graph Neural Network (FP-GNN) is constructed. Extensive testing shows that the FP-GNN model performs exceptionally well in classifying tool path feature points, surpassing existing methods. As a direction application of the proposed method, physical machining examples are conducted, demonstrating that optimizing feed rates in the cross direction—based on identified feature points—improves the continuity of the feed rate in both directions, enhancing the surface machining quality.