A dual-attention graph transformer on a weighted network representation of fusing locations models cross-layer interactions to improve quality prediction in metal additive manufacturing over image, sequence, and graph baselines.
In: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pp
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Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing
A dual-attention graph transformer on a weighted network representation of fusing locations models cross-layer interactions to improve quality prediction in metal additive manufacturing over image, sequence, and graph baselines.