用物理模型指导未来熔池分割，实时预判焊接状态。
Physics-Guided Spatiotemporal State Space Modeling for Lookahead Molten Pool Segmentation in Laser Wire-Feed Welding
- 融合视觉、工艺参数与电学信号，构建时空状态空间模型。
- 500毫秒前瞻下实现74.63% mIoU，关键孔运动感知显著提升精度。
- 适合需要实时闭环控制的高精度激光送丝焊场景。
实时焊池感知对激光送丝焊的闭环控制至关重要，但传感、计算与执行器响应不可避免引入延迟。本文提出一种物理引导的时空状态空间网络，用于焊池未来语义布局的前瞻分割。该模型利用历史同轴灰度图像、焊接工艺参数及对齐的送丝电学信号，预测未来三个物理有意义区域（匙孔、送丝、熔池）的分布。模型结合视觉编码器、工艺与传感器条件特征归一化、块级时序状态空间建模、时域条件隐变量预测、密集未来特征预测及运动感知掩码解码器。辅助的符号距离函数监督、时序一致性约束、特征蒸馏与细粒度匙孔损失进一步规范预测几何与局部运动。在包含43组序列的激光焊接数据集上，所提WeldMamba在500毫秒前瞻下达到74.63% mIoU。消融实验表明，时序历史、块级状态空间建模与匙孔运动感知是实现鲁棒未来分割的关键因素。
原文摘要 · Abstract (English)
Real-time weld-pool perception is critical for closed-loop control in laser wire-feed welding, where sensing, computation, and actuator response introduce unavoidable delay. This paper presents a physics-guided spatiotemporal state space network for lookahead weld-pool segmentation. The model uses historical coaxial grayscale images, welding process parameters, and aligned wire-state electrical signals to predict the future semantic layout of three physically meaningful regions: keyhole, wire, and molten pool. It combines a visual encoder, process- and sensor-conditioned feature normalization, patch-level temporal state space modeling, horizon-conditioned latent prediction, dense future feature prediction, and a motion-aware mask decoder. Auxiliary signed-distance-function supervision, temporal consistency, feature distillation, and fine-grained keyhole losses further constrain the predicted geometry and local motion. Experiments on a 43-sequence laser welding dataset show that the proposed WeldMamba reaches 74.63\% mIoU at a 500 ms lookahead. Ablation studies further show that temporal history, patch-level state space modeling, and keyhole motion awareness are the main contributors to robust future segmentation.
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