## 4 Quality Control
### Visual Quality Inspection  
The existence of surface defects affects the product's appearance and quality in many industry domains. Therefore, one of the most widely applied quality inspection methods in manufacturing is the visual inspection of some aspects of the product. There are many solutions for surface and external defect inspection in many domains of the industry, including the metal, semi-conductor and fiber industries. This section introduces the main directions of visual quality inspection methods supported by machine learning. Figure 5 presents the general model for vision-based product quality inspection according to [104]. In general, defective product identification requires two well-defined steps, i.e., feature extraction and defect identification. Product features could be in the spatial domain or in the transform domain. Moreover, there are several feature extraction methods used in the state-of-the-art, including classification (KNN, Naive Bayes classifier, SVM, Decision trees), clustering (K-means, PCA) and regression (Linear regression, logistic regression) methods. With the advent of deep-learning algorithms, a set of features no longer needs to be designed, such as statistical or spectral features, as opposed to traditional methods. There are several examples in the state-of-the-art surface inspection for supervised (CNN, LSTM) and unsupervised (Autoencoder) learning solutions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Typical Machine Learning Techniques** & **References** \\ \hline Learning mapping between measurements and location & kNN, SVM, Random Forest, XGBoost, Regression tree, neural networks, etc. & [79; 82; 83; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96] \\ Predicting non-LOS & Neural network (CNN, FCN, etc.), SVM, & \\ propagation & Random Forests on channel impulse & [72; 73; 74; 75; 76] \\ Predicting location error & Neural network on channel impulse & [77; 81; 98] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of applications of machine learning techniques in IIoT asset localization. Ref. [105] presents a computer-vision system using machine learning approach to inspect both the internal and external parts of aerospace components. SVM is used to classify defects of the external parts of the product, while for defect classification of the internal parts, a combination of CNN and LSTM is applied. In contrast, ref. [106] presents a quality-level estimation system for the inspection of steel microstructures using the VGG network model. Ref. [107] uses Convolutional Neural Networks (CNN) and Convolutional Autoencoders (CAE) for casting surface inspection. Ref. [108] provides a solution based on supervised learning to inspect press-casting products using CNN, Random Forest, PCA, and XGBoost. Similarly, ref. [109] examines the application of supervised machine learning (random forest, gradient boosting) in defect detection, quality assurance and throughput improvement for optical transceiver manufacturing. Ref. [110] proposes a method for error detection by applying a CNN model to the optical inspection of assembling machines. The CNN-based solution is used in [111] for classifying Pin-in-Paste solder connections with a YOLOv4 architecture. The framework contains highly automatized image data labeling functionality using a Convolutional Autoencoder and near real-time solder joint localization based on a YOLO single-stage detector. Ref. [112] introduces surface defect detection for metal workpieces. The paper introduces both the results of ResNet50 and DenseNet40 architectures. Ref. [104] uses CNN and SVM for defect classification and provides a machine vision model to identify defective products. Ref. [113] introduces semi-supervised deep learning-based surface inspection techniques of labeled data for automated surface defect inspection. Ref. [114] presents an unsupervised clustering method of spatial patterns with wafer map measurement data.