# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 3 Applications of Supervised Learning in Additive Manufacturing
### Quality Detection
investigated defects in surface deformation arising from laser powder bed fusion (LPBF), which can significantly impact the mechanical and physical properties of manufactured AM parts [PERSON] et al. (2018). However, previous research did not address real-time identification of surface deformation. [PERSON]'s study proposed a novel real-time approach to classify surface deformation problems during LPBF processing using a powder bed image as the input for each data sample. Thirteen bar-like geometries were designed and printed using an EOS printer equipped with cameras and sensors to capture each layer during printing to gather data samples. The collected powder bed images were categorized as either normal or defective. The initial dataset consisted of 1022 images converted to grayscale. Since detecting defects early is crucial to halt defect propagation promptly, selecting relevant data samples yielded a final dataset comprising 239 normal and 14 defective images. Given the dataset's significant imbalance, data augmentation methods were employed. They utilized CNN to classify the images. Three distinct model architectures were developed and tested, incorporating techniques such as early stopping, learning rate variation, and employing various model evaluation metrics to refine the model. The results showcased exceptional model performance, achieving an accuracy of up to 99%. The study also concluded that employing a balanced dataset through data augmentation could lead to a more generalized and unbiased ML model. Moreover, [PERSON] et al. devised another real-time CNN model integrated into an automated grading system to oversee the fused deposition modeling process, one of the prominent AM processes [PERSON] et al. (2018). They established an image acquisition system with a high-resolution camera to capture the videos during printing. Subsequently, the data were collected by converting the videos to frames representing 21 classes based on the temperature and speed settings of the AM process. The images were manually inspected to retain only those providing proper views of the printing area. Consequently, a total of 5000 images were obtained, from which a randomly selected subset of 100 images formed the testing set. They employed a CNN architecture within a DL framework, incorporating the aggregation layers, such as convolutional and pooling layers between low-level and high-level layers. Low-level layers were utilized for spatial feature extraction, while high-level layers extracted high-order features or patterns before passing them to the fully connected layers for classification. Various metrics were used to assess the developed CNN model, including accuracy, F1 score, sensitivity, and precision. Remarkably, exceptional accuracies exceeding 93% were achieved for all classes. Additionally, the F1 score indicated relatively varied accuracies among classes with different printing settings. Once the quality predictive model was developed, it was utilized to detect the significance of the defects during the 3D printing process and monitor the process's quality. [PERSON] et al. designed a comprehensive end-to-end workflow for efficiently sorting AM-manufactured parts based on specific post-processing requirements [PERSON] et al. (2018). The initial stage of this automated process involved image rendering, wherein synthetic images were generated from CAD models representing the AM parts. Notably, a variety of simulated camera angles were employed during rendering, accompanied by data augmentation techniques to ensure sufficient diversity in the training data. Subsequently, pre-trained image classification neural networks were adopted and fine-tuned using the training image dataset. In the final step, the trained neural network was deployed to recognize the parts, with the resulting classification recommendations presented to the user via a graphic user interface (GUI). The GUI displayed the top three class predictions for the user to assess and compare with the actual part.