Hierarchical Point Cloud Analysis for 3D Defect Detection of Annular Welds

Abstract

Annular fillet welds on guide rods lie in stress concentration zones and are susceptible to fatigue failure under dynamic suspension loads, necessitating accurate quality inspection. However, their complex three-dimensional topography makes it difficult for conventional methods to balance detection accuracy and efficiency. This paper presents a geometry-driven 3D point-cloud analysis framework integrating weighted geometric template matching, unified height referencing, and decoupled dual-branch defect discrimination. The framework first locates the weld region via weighted template matching with a dynamic early-termination strategy and establishes a unified height datum through multi-stage filtering and RANSAC plane fitting. Defects are then decoupled by height characteristics: extreme height screening isolates oxide inclusions (ISO 6520-1 No. 303), while quantile dual-threshold layered analysis distinguishes insufficient throat thickness (ISO 6520-1 No. 5213) and excessive convexity (ISO 6520-1 No. 503). Experiments on 100 welds sampled across five production batches, evaluated under EN ISO 5817 quality level C, achieve zero false positives for oxide inclusions and insufficient-throat cases and an 88.2% detection rate for excessive convexity, delivering robust performance under the tested conditions.