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To address the challenges in evaluating assembly quality for revolved thin-walled parts, a novel algorithm was developed that combines virtual assembly and quality assessment. Leveraging 3D point cloud data, the algorithm employs various techniques for accurate assembly assessment. Initial registration using an Oriented Bounding Box (OBB) achieved rough alignment, followed by precise registration using point-to-plane Iterative Closest Point (ICP) to minimize RMS error to 0.173mm. Contour points on cross sections were extracted through resampling. Transverse section contours were effectively fitted using least square circle fitting. However, due to its randomness, least square ellipse fitting for longitudinal section contours struggled to meet precision standards. To quantify part manufacturing errors, RMS radial distances from longitudinal section contour points to theoretical ellipses were utilized. The algorithm then calculated the assembly sequence with the least error. Detection of assembly interference was realized by measuring distances between transverse section contour boundary points. Simulated point cloud data validated the algorithm's efficacy in effectively assessing part quality, optimizing assembly sequences, and identifying assembly interference. By combining innovative registration, contour analysis, and error quantification techniques, the algorithm offers a promising solution for ensuring assembly quality and reliability, addressing the challenges posed by irregular revolved thin-walled parts.
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