WeldMamba achieves 74.63% mIoU for 500 ms lookahead segmentation of keyhole, wire, and molten pool using spatiotemporal state space modeling conditioned on welding signals and physics-based losses on a 43-sequence dataset.
How to accurately monitor the weld penetration from dynamic weld pool serial images using CNN-LSTM deep learning model?IEEE Robotics and Automation Letters, 7(3):6519–6525, 2022
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A CNN-plus-state-space-model multi-task network predicts laser weld penetration state (99.35% accuracy), depth (1.79 mm error), and cross-section morphology (95.65% accuracy) from top-view weld-pool images and welding parameters.
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Physics-Guided Spatiotemporal State Space Modeling for Lookahead Molten Pool Segmentation in Laser Wire-Feed Welding
WeldMamba achieves 74.63% mIoU for 500 ms lookahead segmentation of keyhole, wire, and molten pool using spatiotemporal state space modeling conditioned on welding signals and physics-based losses on a 43-sequence dataset.
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A multi-task spatiotemporal deep neural network for predicting penetration depth and morphology in laser welding
A CNN-plus-state-space-model multi-task network predicts laser weld penetration state (99.35% accuracy), depth (1.79 mm error), and cross-section morphology (95.65% accuracy) from top-view weld-pool images and welding parameters.