Process monitoring by deep neural networks in directed energy deposition: CNN-based detection, segmentation, and statistical analysis of melt pools

The complex interaction between laser and material in Laser Wire Direct Energy Deposition (LW-DED) Additive Manufacturing (AM) benefits from process monitoring methods to ensure process stability and final part quality. Understanding the relationship between process parameters and melt pool geometrical characteristics can be used to effectively monitor and in-process control the process, as the melt pool geometrical characteristics serve as crucial indicators of process stability and quality. This study presents a novel in-situ monitoring approach for LW-DED, utilizing process images for melt pool segmentation, melt pool geometrical characteristics estimation, process stability assessment, and bead geometry prediction. The segmentation of melt pool objects was successfully accomplished using Convolutional Neural Networks (CNN)-based models, enabling the prediction of essential parameters such as melt pool area, height, width, center of area, and the center point of the bounding box enclosing the melt pool. Multiple models were compared regarding the accuracy and processing speed using a controlled central composite design and random experiments. We used an Inconel alloy 625 wire and two distinct substrate materials for deposition, a coaxial laser welding head with a 3 kW fiber laser, and an off-axis welding camera for monitoring. Among the CNN architectures evaluated, YOLOv8l demonstrated superior accuracy with mean Average Precision (mAP) values of 0.925 and 0.853 for Stainless Steel (SS) and low carbon steel (S355) substrates, respectively. Additionally, YOLOv8s exhibited a notable processing speed of over 114 frames per second, which indicates its suitability for real-time process control. Furthermore, the results indicate a significant correlation between process parameters and melt pool geometry variables. Notably, a clear correlation was established between melt pool characteristics and bead geometries obtained through microscopic examinations, including penetration depth and heat-affected zone. The analysis revealed a significant correlation for the bead area and width parameters. In relation to the bead height, while the correlation exhibited a lower magnitude compared to bead area and width, it remained responsive. In addition, the tensor masks derived from the developed models have proven to be highly effective in accurately predicting bead geometries. The results demonstrate the effectiveness of YOLO-based algorithms for detecting and segmenting the melt pool. Statistical analysis confirms the significance of stabilized process data and the accuracy of melt pool geometric models. We demonstrate that integrating advanced monitoring and control techniques using artificial intelligence methods like CNN can facilitate process stability and quality control.