On the application of YOLO-based object detection models to classify and detect defects in laser-directed energy deposition process

Abstract Reducing the defects in the additively manufactured components using Laser-Directed Energy Deposition (L-DED) process is important for ensuring structural integrity, surface quality, and functional performance. The first required step for reducing defects in the L-DED manufactured components is the identification and understanding of the type of defects using the object detection approach. This paper aims to use a YOLO-based object detection models to classify and detect defects in the horizontal wall, vertical wall, and cuboid structures manufactured using various combinations of L-DED process parameters. The objectives involved are training, testing and validating of YOLOv7, YOLOv8, YOLOv9, and YOLOv9-GELAN models on the independent dataset of defects such as flash formation, void and rough texture, identifying the best YOLO model capable of detecting small and big size multiple defects within a single image and comparing the defects captured by YOLO model with previously used conventional CNN model such as VGG16. The results revealed that YOLOv9-GELAN exhibited good performance indicators compared to other YOLO models. The increasing trend for mAP0.5:0.95 signifies YOLOv9-GELAN as a good choice for defect detection of multiple defects in a single image. It also gave mAP of 95.7%, precision of 94%, recall of 96%, and F1-score of 90%, indicating accuracy in defect localisation and classification with minimal false positives and negatives. These high values for YOLOv9-GELAN indicate its capability to accurately highlight the defects using the bounding box compared to the previously proposed VGG16 model. In addition, YOLOv9-GELAN capability of processing 62 images per second showed its potential for higher frames processing compared to other YOLO models. This research will progress the development of AI-based in-situ defect monitoring for the L-DED process.