Multi-source information fusion for enhanced in-process quality monitoring of laser powder bed fusion additive manufacturing

Abstract

Defects such as lack of fusion, porosity, and keyhole generated during the laser powder bed fusion (L-PBF) additive manufacturing process pose a challenge, with the absence of effective prediction methods for the process-induced defects and as-printed quality. On-line monitoring becomes imperative to evaluate and enhance the L-PBF in-process quality. Here, a multi-source information fusion strategy using a residual network (ResNet) is introduced for the in-process monitoring during the L-PBF. This approach integrates the melt-pool infrared (IR) images captured layer-by-layer, melt-track top-view photographs, melt-track numerical simulation diagrams, L-PBF process parameters, and characteristic parameters of melt-pool cross-sectional morphology after solidification to enable quality monitoring of the L-PBF processing. To assess the defect severity, a quantitative defect evaluation method based on the defect-specific characteristics is proposed. This method facilitates the quantitative evaluation of defects by extracting pertinent defect indicators related to porosity and deformation. Additionally, two types of residual physical hybrid networks (ResPHN) and two types of residual physical fusion supervisory networks (ResPFSN) are introduced in this study. The performance of these four network models is meticulously compared and evaluated. The findings reveal that the most effective feature fusion monitoring model is the ResPFSN-type2, achieving an impressive accuracy of 99.4 % and displaying consistent performance across varying input image sizes and training data volumes. It underscores its potential for real-time process control applications. Furthermore, the interpretability of the model is scrutinized, with results indicating that the ResPFSN-type2 model adeptly identifies the contour texture and local features of the laser-induced melt pools.