In Situ Monitoring and Innovative Feature Fusion Neural Network for Enhanced Laser-Directed Energy Deposition Track Geometry Prediction and Control

The printed quality of laser-directed energy deposition (L-DED) technology is significantly influenced by the characteristics of the molten pool. Therefore, it is crucial to perform in situ monitoring of the molten pool and accurately predict track geometric features in order to effectively control the processing quality. In this study, an innovative feature fusion deep learning (FF-DPL) network is proposed, which combines both dynamic image features and static processing parameters to significantly improve the prediction accuracy by utilizing only a smaller dataset and requiring fewer data features. Width average prediction accuracy reaches an impressive 98.45%. It is worth noting that on the extrapolation test dataset, the FF-DPL model achieves an average prediction accuracy in depth and height that is over 10% higher than the other six popular deep learning models. The extracted features of the images under different convolutional layers are visualized, confirming the significance of spatter and steam plume features for track geometry prediction. Finally, a correlation analysis is performed to investigate the relationship between the input parameters and the track geometry, revealing the influence trends and magnitudes of different processing parameters on the variation of the track geometry, which provides important guidance for controlling the molten pool size in practical engineering production.