Dual-branch convolutional neural network with attention modules for LIBS-NIRS data fusion in cement composition quantification.

Background: Cement composition, including key oxides such as CaO, SiO, AlO, and FeO, plays a critical role in determining cement's strength and durability. Real-time monitoring of these components during cement production is essential for ensuring optimal raw material ratios. Spectroscopic techniques, such as Laser Induced Breakdown Spectroscopy (LIBS) and Near Infrared Spectroscopy (NIRS), offer significant potential for rapid and non-destructive cement analysis, but their individual limitations, such as matrix effects in LIBS and spectral overlap in NIRS, necessitate an integrated method to achieve accurate and stable results.

Results: In this study, we propose a novel fusion method based on a dual-branch convolutional neural network with an attention module (DBAM-CNN) to synergize LIBS and NIRS data for enhanced cement component quantification. The dual-branch CNN structure enables feature extraction of atomic and molecular information from LIBS and NIRS data, respectively, optimizing the global task of improving quantitative analysis by capturing complementary information from both spectroscopic techniques. These features are then fused, and spatial and channel attention modules are used to refine the feature weights, enabling the model to effectively capture spectral fingerprint information. Experimental results show that the DBAM-CNN outperforms both existing fusion strategies and single technologies, demonstrating exceptional performance in real-time, high-precision cement composition analysis. SHAP analysis further reveals that the method highlights key features in LIBS and NIRS, leading to enhanced quantitative outcomes.

Significance: The proposed DBAM-CNN method significantly enhances cement composition analysis by effectively integrating complementary information from LIBS and NIRS. By addressing issues such as information redundancy and feature loss that are common in existing fusion strategies, this approach offers a more reliable and efficient solution for real-time, high-precision monitoring in cement production. It represents an advancement in spectroscopic data fusion techniques, paving the way for improved cement quality control.