Physics-aware adaptive gated fusion for robust industrial smoke monitoring via acoustic-optical multimodal LIBS

To address the critical challenges of flow field heterogeneity and stochastic signal fluctuations in the online monitoring of industrial aerosols, this paper proposes a novel, to the best of our knowledge, Laser-Induced Breakdown Spectroscopy (LIBS) enhancement technique based on acoustic-optical multimodal synergistic perception. Departing from conventional monitoring paradigms that rely solely on spectral data, this study introduces the acoustic signatures of laser-induced shockwaves as a novel physical dimension to elucidate the transient interaction mechanisms between the laser and microscopic particles. The developed Physics-Aware Adaptive Gated Fusion (P-AGF) network enables deep mining and dynamic complementarity of spectral and acoustic information. Experimental results demonstrate that the P-AGF model can precisely identify the microscopic interaction states between the laser and particles in complex aerosol scenarios, such as soldering smoke, achieving a test set accuracy of 92.22%. This method overcomes unimodal detection limits in mixed-phase media, significantly enhancing LIBS stability and robustness for intelligent industrial monitoring.