Complex parts machining path planning through cross-source point cloud processing

Abstract

When cleaning cast parts, production machinery such as robots and smart machines must be able to operate tools along appropriate machining paths. Planning the machining path using 3D point cloud processing techniques is a suitable approach. The local point clouds generated from in situ scans and the standard point clouds, however, differ in scale and accuracy, and these issues make the registration of cross-source point clouds a difficult academic topic. The study presented here suggests a novel cross-source point cloud registration technique that combines a point cloud slicing algorithm and a curve fitting algorithm to plan machining paths for complex cast parts. The results demonstrate the practicality of the proposed method, with the uniformity of the surface profile and the operational effectiveness of the machining process being 57.90% and 67.74% higher, respectively, for the machining paths generated using the proposed method compared to the manual method. By comparing trials of various machining path curve fitting algorithms, we find that the Fourier fitting approach performs best. In addition, the co-planar features chosen for coarse registration will be better registered if they are closer to the plane containing the intended machining path. The proposed method may be useful for online inspection by UAVs and interactive robots.