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A method for reconstructing large-scale part models based on feature point cloud data
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AI
DOI：10.1007/s10845-025-02717-y.png)
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Abstract
En 中文
High-fidelity digital twin models of physical entities form the cornerstone of optimized assembly processes. Hull components are characterized by their large scale, substantial weight, and high complexity. Due to the parts manufacturing error and welding deformation, the hull rib plate often gives rise to interference because of the out-of-tolerance of assembly clearance, which has seriously affected the efficiency and quality of ship assembly. Therefore, combining feature point cloud recognition and feature stitching technology, a new fast reconstruction method for large-scale parts is proposed. Firstly, based on the assembly process of parts, an assembly feature library composed of historical data and expert experience is established, and then the constraint features are determined in assembly processing. Training the theoretical assembly model, an improved deep learning network for recognition and segmentation is created. Finally, by utilizing a feature stitching technique that integrates plane clustering segmentation and convex hull algorithm, the large-scale model is reconstructed. Taking a hull block component as an example, the reconstruction speed is improved by 30%.
Keywords:
Large-scale parts
Model reconstruction
Feature point cloud
Feature stitching
Assembly process
Journal
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