DMAW: A Dynamic Multimodal Measurement Fusion Network with Attention for Reliable Welding Process Monitoring under Harsh Industrial Environments

In laser welding, harsh environmental conditions such as arc light interference, spatter, and strong background noise pose significant challenges to accurate process monitoring and measurement. Traditional single-modality sensing methods are often inadequate for a comprehensive characterization of welding states and remain highly vulnerable to noise contamination. To overcome the limitations, a dynamic multimodal attention-based weighting (DMAW) network is proposed, which integrates visual and acoustic information to achieve more comprehensive welding state characterization. First, modality-specific feature extractors are trained using unsupervised knowledge distillation (KD) to learn welding-relevant semantic representations. Next, the extracted features are passed through a cross-attention module to enable intermodal interaction and suppress noise. Finally, a dynamic reliability-weighted fusion is proposed that adaptively adjusts modality contributions, thereby reducing measurement uncertainty and enhancing robustness under varying conditions. Experimental validation on a dedicated laser welding platform demonstrates that the proposed framework achieves superior accuracy and resilience in welding state monitoring, highlighting its potential as a reliable solution for intelligent industrial welding systems.