Exploiting deep learning and augmented reality in fused deposition modeling: a focus on registration

Abstract

The current study aimed to propose a Deep Learning (DL) based framework to retrieve in real-time the position and the rotation of an object in need of maintenance from live video frames only. For testing the positioning performances, we focused on intervention on a generic Fused Deposition Modeling (FDM) 3D printer maintenance. Lastly, to demonstrate a possible Augmented Reality (AR) application that can be built on top of this, we discussed a specific case study using a Prusa i3 MKS FDM printer. This method was developed using a You Only Look Once (YOLOv3) network for object detection to locate the position of the FDM 3D printer and a subsequent Rotation Convolutional Neural Network (RotationCNN), trained on a dataset of artificial images, to predict the rotations’ parameters for attaching the 3D model. To train YOLOv3 we used an augmented dataset of 1653 real images, while to train the RotationCNN we utilized a dataset of 99.220 synthetic images, showing the FDM 3D Printer with different orientations, and fine-tuned it using 235 real images tagged manually. The YOLOv3 network obtained an AP (Average Precision) of 100% with Intersection Over Unit parameter of 0.5, while the RotationCNN showed a mean Geodesic Distance of 0.250 (σ = 0.210) and a mean accuracy to detect the correct rotation r of 0.619 (σ = 0.130), considering as acceptable the range [r − 10, r + 10]. We then evaluate the CAD system performances with 10 non-expert users: the average speed improved from 9.61 (σ = 1.53) to 5.30 (σ = 1.30) and the average number of actions to complete the task from 12.60 (σ = 2.15) to 11.00 (σ = 0.89). This work is a further step through the adoption of DL and AR in the assistance domain. In future works, we will overcome the limitations of this approach and develop a complete mobile CAD system that could be extended to any object that presents a 3D counterpart model.