Abstract With rapid advances in 3-dimensional (3D) metrology, point cloud data are increasingly available for surface quality inspection in additive manufacturing (AM). Compared to images, point clouds capture richer geometric information for characterizing surface anomalies, enabling more comprehensive defect diagnosis and mitigation. However, it remains challenging to extract anomaly-pertinent information from scanned point clouds, due to 1) the scarcity of annotated point cloud data for training robust anomaly detection models and 2) the inherent complexity of point cloud processing, stemming from their high dimensionality, high volume, and unstructured nature. To address the challenges, this study develops a new framework for self-supervised representation learning of point clouds to glean anomaly-pertinent features. Specifically, a graph contrastive learning scheme is constructed by integrating l-hop subgraphs, hard-negative sampling, and graph neural networks (GNN) to explore the self-similarity of AM-fabricated surface patterns and highlight anomaly-induced variations. Unlike most existing approaches, it requires no external training samples or manual annotations. The framework has been evaluated using simulations and real-world data collected from wire arc additive manufacturing (WAAM). Results demonstrate that it outperforms the state-of-the-art benchmarks in accurately locating and charactering surface defects, including subtle ones. The developed framework has strong potential for broader applications in differentiating surface textures and geometric patterns across diverse AM processes.
Wang et al. (2026) studied this question.