Review of Defect Detection Methods for Thin-Walled Parts Based on Point Cloud Data Processing

Abstract

Thin-walled parts are widely used in various fields of industrial production, but their inherent characteristics, such as low stiffness and susceptibility to deformation, make them prone to various defects during use. Thin-walled covering parts, which serve as thin shells or covers to protect other components, are extensively utilized in aerospace, automotive, and shipbuilding industries. The condition of these parts is directly related to the structural integrity, performance stability, and operational safety of the associated components and equipment. Due to their direct exposure to external environments, thin-walled covering parts are vulnerable to damage. Traditional defect detection methods, such as manual visual inspection and 2D imaging, have limitations in addressing these challenges. To improve the speed and accuracy of damage detection in thin-walled covering parts, this paper proposes a defect detection method based on point cloud data processing. This approach involves collecting point cloud data of the thin-walled parts, applying preprocessing techniques to remove noise, and utilizing feature extraction and machine learning algorithms to achieve automatic detection and classification of defects. The research results demonstrate that this method offers high accuracy and efficiency, effectively meeting the quality inspection needs for thin-walled parts in industrial production.