Melt pool super solution reconstruction based on dual path deep learning for laser powder bed fusion monitoring

Melt pool monitoring in laser powder bed fusion (L-PBF) is an important foundation for process control and melting dynamics research. However, due to hardware limitations and the intense metallurgical phenomena during the melting process, the quality of the melt pool monitoring images cannot be guaranteed. This paper proposes a deep learning-based melt pool image super-resolution (SR) reconstruction method based on the particularity of melt pool images. It is an innovative dual-path structure. The first branch utilizes residual-in-residual structures and the efficient channel attention-Net attention mechanism to achieve the adaptive feature extraction of high-frequency information, thereby capturing effective melt pool boundary information. The second branch uses the U-Net structure to address the low utilization of overall characteristics of the melt pool in the chain-based SR network. In addition to the universal peak signal-to-noise ratio and structural similarity index measure metrics, the reconstruction accuracy of melt pool contour features is used to measure model performance, which intuitively reflects the significance of this work for melt pool monitoring. The results demonstrate that the proposed SR reconstruction method enhances the resolution and clarity of melt pool images. Furthermore, SR reconstruction of the melt pool effectively reduces errors in extracting melt pool features. This method provides a network paradigm for high-precision L-PBF monitoring. It integrates important boundary information and overall morphology features of the melt pool through a dual path structure, thereby achieving reliable SR reconstruction. This research will contribute to low-cost in situ monitoring of L-PBF and subsequent process control.