# A Synergic Approach of Deep Learning towards Digital Additive Manufacturing: A Review
## 4 Deep Learning Models in Additive Manufacturing
### Other Deep-Learning Networks  
In addition to the above-described deep learning model, there are a few more DL algorithms that have been used, such as radial basis function networks (RBFNs), self-organizing maps (SOMs), multilayer perceptrons (MLPs), etc. However, a significantly less prominent use case was present while doing a literature survey on these models. Much work has been done using MLP, but it has some limitations over CNN. Object detections and segmentations are used in defect detections in AM. [PERSON] et al. used the YOLO object detection deep learning model for defect detection in adaptive manufacturing. It enables rapid and precise flaw identification for wire and arc additive manufacturing (WAAM). Yolo algorithm performances are compared with a traditional object detections algorithm. It shows that it can be used in real-world industrial applications and has the potential to be used as a vision-based approach in defect identification systems [110]. [PERSON] et al. discuss how we improve classifying the product quality in AM by using the YOLO algorithm. The outcome shows that 70% of product quality is classified in Realtime video. The YOLO algorithm performances are compared with different version from version 2 to version 5 and the YOLO algorithm reduces the labor cost [111]. [PERSON] et al. developed center net-based defect detection for AM. The center net uses object size, a heatmap, and a density map for defect detection. The suggested model, Center Net-CL, outperforms traditional object detection models, such as one-stage, two-stage, and anchor-free models, in terms of detection performance. Although this strategy worked effectively, it is only applicable in certain sectors [112]. The semantic segmentation framework for additive manufacturing can improve the visual analysis of production processes and allow the detection of specific manufacturing problems. The semantic segmentation work will enable the localization of 3D printed components in picture frames that were collected and the application of image processing techniques to its structural elements for further tracking of manufacturing errors. The use of image style transfer is highly valuable for future study in the area of converting synthetic renderings to actual photographs of 3D printed objects [113]. [PERSON] et al. reported the challenges of segmentations in AM. The image size is very small and the appearance of defect variations is also very small, so it is very difficult to detect defects in AM. Three-dimensional CNN achieved good performances in volumetric images. A 3D U-Net model was used to detect errors automatically using computed tomography (XCT) pictures of AM specimens [114]. [PERSON] et al. presented anunsupervised deep learning algorithm for defect segmentations in AM. The unsupervised models extract local features as well as global features in the image for improving the defect segmentations in AM. A self-attention model performs better than the without-self-attention model for defect detection in AM [115]. Job scheduling is the biggest problem in AM. The order in which the job is scheduled is to be decided for the better performance of AM. Deep reinforcement learning can be applied to decide the job orders. Traditional approaches need a lot of time since they