Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing

• URL: http://arxiv.org/abs/2606.10227v1
• Date: Mon, 08 Jun 2026 22:39:13 GMT
• Title: Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing
• Abstract summary: We develop a novel graph transformer for modeling 3D neighborhood interactions and learn their effects on build quality in metal additive manufacturing.<n>Results show that the proposed framework significantly outperforms image-based, sequence-based, and graph-based models in characterizing process-quality relationships.
• Score: 5.419070059894391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions induced by repeated layer-wise melting, solidification, and reheating across the 3D build. Advanced sensing provide a great opportunity to collect rich observations of the actual manufacturing process for real-time quality monitoring and control. Yet, existing methods often have limited ability to represent multi-layer interactions and quantify their contributions to quality. In this paper, we develop a novel spatiotemporal graph transformer for modeling 3D neighborhood interactions and learn their effects on build quality in metal additive manufacturing. Specifically, we first introduce a weighted network representation of the manufacturing process, where fusing locations are modeled as nodes, and their spatial- and process-dependent relationships are encoded as edge weights. This representation also enables the integration of multimodal data (e.g., geometric design, process settings, and in-situ sensing data) into a unified structure for downstream learning tasks. Building on this network, we further design a dual-attention graph transformer that captures both within-node feature dependencies and cross-node neighborhood interactions for quality representation learning. Experimental results show that the proposed framework significantly outperforms image-based, sequence-based, and graph-based models in characterizing process-quality relationships. More importantly, the incorporation of cross-layer interactions is critical for improving quality prediction performance. This framework is broadly applicable to other tasks involving network modeling and graph-based representation learning.

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