🤖 AI Summary
Conventional geometric accuracy monitoring of unstructured point cloud data (PCD) suffers from error accumulation, high computational cost, and artifact generation due to reliance on registration and mesh reconstruction. Method: This paper proposes a preprocessing-free, intrinsic geometry-aware monitoring method that directly extracts intrinsic geometric features from raw PCD via Laplacian eigenmaps and geodesic distance computation. It incorporates an unsupervised threshold selection mechanism and a dual-strategy feature learning framework to enable robust defect identification. Contribution/Results: Experimental results demonstrate that the method achieves efficient and accurate detection of diverse geometric defects—including dents, bulges, and misalignments—without registration or reconstruction. It maintains high precision (>96% F1-score) and strong stability across heterogeneous manufacturing scenarios (e.g., additive manufacturing, CNC machining), reducing monitoring time by up to 78% compared to state-of-the-art approaches while eliminating reconstruction-induced artifacts. The framework significantly enhances both efficiency and reliability in industrial PCD-based quality inspection.
📝 Abstract
Modern sensing technologies have enabled the collection of unstructured point cloud data (PCD) of varying sizes, which are used to monitor the geometric accuracy of 3D objects. PCD are widely applied in advanced manufacturing processes, including additive, subtractive, and hybrid manufacturing. To ensure the consistency of analysis and avoid false alarms, preprocessing steps such as registration and mesh reconstruction are commonly applied prior to monitoring. However, these steps are error-prone, time-consuming and may introduce artifacts, potentially affecting monitoring outcomes. In this paper, we present a novel registration-free approach for monitoring PCD of complex shapes, eliminating the need for both registration and mesh reconstruction. Our proposal consists of two alternative feature learning methods and a common monitoring scheme. Feature learning methods leverage intrinsic geometric properties of the shape, captured via the Laplacian and geodesic distances. In the monitoring scheme, thresholding techniques are used to further select intrinsic features most indicative of potential out-of-control conditions. Numerical experiments and case studies highlight the effectiveness of the proposed approach in identifying different types of defects.