Interpretable semi-supervised 3D deep anomaly detection for surface defect localization in wire-laser directed energy deposition using point clouds

Wire-laser directed energy deposition (WL-DED) enables the fabrication of large-scale metallic components but frequently suffers from process-induced surface defects that hinder part quality and increase post-processing costs. Automated inspection is challenging because defects are diverse and rare, making large labelled datasets impractical. This paper proposes an interpretable semi-supervised framework for surface detection and localization on WL-DED components using high-density 3D point clouds acquired by laser scanning. The workflow includes point-cloud preprocessing, patch-based segmentation, voxelization, and semi-supervised representation learning of defect-free surface morphology. Two 3D deep autoencoder models, i.e., a convolutional autoencoder (CAE) and a variational autoencoder (VAE), are trained exclusively on normal patches and detect anomalies through voxel-wise reconstruction errors. Defects are localized by mapping reconstruction-error heatmaps back onto the original surface, enabling quantitative visualization of defect severity. Experimental results on WL-DED thin-wall samples show that the optimized CAE achieves 86.09% precision, while the VAE reaches 86.43% precision with improved defect localization (mIoU up to 0.7234). Activation-map analysis provides interpretability by highlighting geometric regions that drive anomaly responses. A hyperparameter study demonstrates that lower voxel resolutions and smaller patch sizes improve robustness and reduce false positives. The proposed framework generalizes to more complex multi-bead, multi-layer structures with minimal retraining, supporting practical deployment for intelligent inspection and decision-making in additive manufacturing quality assurance.