Data model-based toolpath generation techniques for CNC milling machines - Liao, Jianbin; Huang, Zeng (Frontiers in Mechanical Engineering, 2024)
With the development of computer technology and the widespread application of data models, toolpath generation techniques utilizing data models have become increasingly prominent. To delve deeper into this field, this research focuses on point cloud models as the foundation for data models. Firstly, a four-point denoising method and octree method are employed for noise reduction and down-sampling of the point cloud model. Subsequently, the layer slicing method is utilized for computational analysis of scattered point cloud models to obtain toolpath trajectories during rough machining. The residual height method is applied to analyze curved surface point cloud models, extracting toolpath trajectories for finish machining. Experimental results demonstrated that the proposed rough machining toolpath generation method achieved a minimum error close to 10% with a slicing thickness of 5mm, and the shortest computation time was 35 seconds. The finish machining toolpath generation method exhibited a minimum error rate of 10.17%, a computation time of 11.82 seconds, and a maximum trajectory smoothness of 83.49%. In conclusion, compared to similar trajectory generation methods, the two proposed methods showed superior performance, enabling more efficient toolpath generation and providing theoretical support for the technological development of computer numerical control milling machines.