# Machine Learning in Manufacturing towards Industry 4.0: From For Now' to 'Four-Know'
## 4 Machine Learning Methods and Applications
### Supervised Learning Methods
Markov chains can be utilized in manufacturing to model and analyze the behavior of systems (Know-why, system level) such as production lines [99] or supply chains [100]. In addition, the capability of predicting future states with Markov chains enables applications predicting joint maintenance in production systems [101] (Know-when, system level) and optimizing production scheduling [102] (Know-how, system level). _Kernel-based methods_: As depicted in Figure 8, kernel-based methods utilize a defined kernel function to map input data into a high-dimensional implicit feature space [103]. Instead of computing the targeted coordinates, kernel-based methods normally compute the inner product between a pair of data points in the feature space. However, kernel-based methods have low efficiency, especially with respect to large-scale input data. Due to the promising capability of kernel-based methods in classification and regression, they can be utilized in the Know-what and Know-when stages in manufacturing, such as defect detection [104] (Know-what, product level), quality prediction [105] (Know-when, product level), and wear prediction in machinery [106] (Know-when, machine level). There are different types of kernel-based methods in supervised learning, such as SVM [107] and Kernel-Fisher discriminant analysis (KFD) [108]. Figure 8: The principle of kernel-based methods. Using a kernel, the linearly inseparable input data are transformed to another feature space in which they become linearly separable. Figure 7: The principle of a decision tree. As shown, the feature space is partitioned into several rectangles in which the input point can find the corresponding class. _Neural-network-based methods_: Inspired by biological neurons and their ability to communicate with other connected cells, neural network-based methods employ artificial neurons. A typical neural network, such as ANNs, consists of an input layer, hidden layer, and output layer, as illustrated in Figure 9. Common ANNs types include CNNs [109], RNNs [110], and Deep Belief Network (DBN) [111]. Thanks to their powerful feature extraction capability when using matrix-like data, CNNs are widely used for image processing. In terms of possible applications in manufacturing, CNNs can be used in the Know-what stage to perform image-based quality control [112] (Know-what, product level) or image-based process monitoring [113] (Know-what, process level). In addition, by converting time series data from sensors to 2D images [114], CNNs can be used to detect and diagnosis machine failure as well. RNNs are typically used to process sequential input data such as time series data or sequential images. Therefore, in terms of possible applications in manufacturing, RNNs are well-suited to the Know-when stage for analyzing sensor data or live images from machines, processes, or production systems. For instance, RNNs can enable the real-time performance prediction, such as the remaining useful life of machinery [115] (Know-when, machine level), process behavior prediction [116] (Know-when, process level), or the prediction of production indicators for real-time production scheduling [117] (Know-when, system level). The typical supervised learning approaches applied in manufacturing are summarized in Table 11.