Synthetic Data-Driven Mixed Reality for AR-Assisted Maintenance

AI-assisted augmented reality (AR) tools hold significant promise for improving industrial maintenance and assembly workflows, particularly for training novice technicians, amid a shortage of experienced personnel in the Aerospace sector. This work introduces an end-to-end methodology for developing AI-assisted AR tools only using 3D CAD data. The proposed workflow is especially valuable to industries-like Aerospace-that manage comprehensive 3D data. The proposed pipeline uses a context-oriented training strategy using fully assembled CAD models to generate domain-randomized datasets for component recognition. At runtime, assembly pose data is used to project regions of interest onto the image. The trained component recognition model verifies if the component is installed within each projected region. To validate the data generation methodology, a sample gearbox containing four unique components was modeled to produce a synthetic dataset of 13,000 images, which was used to train Faster R-CNN for component recognition. The model was evaluated on a training set consisting of 112 real images of the gearbox. After 30 epochs of training, the component recognition model-evaluated on the test set across different IoU thresholds (0.50-0.95) and confidence scores (0.05-0.95)-demonstrated state of the art precision and recall results based on dataset composition. This work demonstrates that AI-assisted AR tools can be realized entirely from synthetic data produced through the proposed pipeline, removing the traditional bottleneck of real-world image collection and manual annotation.