Real-time prediction of deposited bead width in L-DED using semi-supervised transfer learning

Laser directed energy deposition (L-DED) is an additive manufacturing (AM) technology that offers unique advantages for creating and repairing metallic parts. The quality of the final print is highly dependent on the consistency of the printing parameters, and as a result, the deposited bead geometry along the printing pathway. Inconsistencies, particularly in the bead width, can produce defects between adjacent beads, increasing the material susceptibility to failure. Therefore, it is crucial to monitor and predict the printing parameters in real time during the process. The objective of this study is to develop a machine learning model that accurately predicts the width of the deposited track in real time. To achieve this, a dataset with approximately 12,379 melt pool images was used to train the algorithm and predict the width of the deposited track. Each one of the deposited track was measured by a profilometer at 10 points along the pathway, and intermediate labels were predicted using a spline curve, making the model semi-supervised. To implement the machine learning model, pre-trained frozen networks based on convolutional neural networks (CNN) architectures VGG, ResNet, and DenseNet were employed, using transfer learning principles. These networks were integrated into a dense, fully connected network containing trainable parameters. The results demonstrate a good model fit, with a mean absolute error of 4.5% and a mean absolute error of 0.0358 mm. Moreover, the processing frequency of the best model, at 55 Hz, enables real-time control of the L-DED manufacturing process. It is important to highlight, however, that the VGG-based model shows a frequency of 250 Hz and similar results. Thus, accurately predicting the width of the deposited bead has the potential to significantly improve the quality of the final part by reducing overall defects, such as lack of fusion and porosity, whenever this data is used as input to a feedback control system. This is particularly valuable in industries where mechanical properties and fatigue life are critical, such as automotive, aerospace, and biomedical sectors. The models developed in this study offer a promising approach to effectively provide data to control the L-DED manufacturing process in real time, enabling the reliable production of high-quality components associated with reduced manufacturing costs.