Cross-modality transfer for DED-LB/M: AI-based prediction of schlieren phenomena from coaxial imaging

Abstract

Real-time process monitoring is essential for ensuring stability and defect control in directed energy deposition - laser beam/metal (DED-LB/M). Schlieren imaging has proven to be a valuable tool for detecting refractive index variations in the process zone, providing insights into gas flow behaviour, shielding gas efficiency and process plume dynamics. However, schlieren imaging typically requires specialized optical setups, making integration into industrial systems challenging. This study explores an artificial intelligence-driven cross-modality transfer approach that enables the prediction of schlieren-induced refractive index variations from coaxial imaging data, eliminating the need for a dedicated schlieren setup. A background-oriented schlieren system was used to capture reference data, while a coaxial camera recorded the melt pool and surrounding process zone during DED-LB/M. A machine learning model was trained on the combined dataset, establishing correlations between schlieren activity and intensity variations in the coaxial images. The model successfully predicted schlieren-induced disturbances, allowing for the indirect detection of gas flow instabilities and shielding gas deficiencies. The results demonstrate that artificial intelligence-based analysis of coaxial imaging can provide schlieren-equivalent process information, making it possible to monitor refractive index variations, detect process deviations and improve defect prediction in real time. This approach enhances process monitoring capabilities in DED-LB/M, enabling cost-effective, scalable and easily integrable monitoring solutions for industrial applications.