Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing
Researchers have developed a novel spatiotemporal graph transformer designed to model complex interactions in metal additive manufacturing. This framework represents the manufacturing process as a network, allowing for the integration of multimodal data and capturing both within-node feature dependencies and cross-node neighborhood interactions. Experiments demonstrate that this approach significantly outperforms existing models in characterizing process-quality relationships, with cross-layer interactions proving critical for accurate quality prediction. AI