## Literature Review
### Machine Learning Methods in Manufacturing  
The manufacturing industry is experiencing an increase in data generation, e.g., sensor data from the production line, environmental data, etc. New developments in technology such as machine learning offer great potential to analyze data repositories, and thus can provide support for management in decision-making or can be used to improve system performance. Machine learning techniques are utilized to detect certain patterns or regularities and have been successfully implemented in various areas such as fault detection, quality prediction, defect classification, and visual inspection. Several studies have utilized machine learning and showed significant results in the manufacturing industry. [PERSON] et al. employed seven different machine learning-based novelty detection methods to detect faulty wafers [43]. The models were trained with Fault Detection and Classification (FDC) data to detect faulty wafers. The experimental results showed that machine learning-based models had a high possibility of detecting faulty wafers. [PERSON] et al. performed an evaluation analysis on four machine learning algorithms (i.e., decision tree, random forest, artificial neural network, and support vector machine) for predicting the quality of metal castings product [14]. The result showed that all of four machine learning algorithms can effectively be used to predict the quality of product. [PERSON] et al. utilized support vector machine algorithm to predict the quality of welding in a high-power disk layer [44]. The results showed that the proposed quality prediction model can be used for real-time monitoring system. An intelligent system was developed by [PERSON] et al. to minimize the incorrect warning in detecting the quality of product in manufacturing [45]. They utilized three methods (i.e., visual inspection, support vector machine, and similarity matching). Through real-case implementation in manufacturing company in Taiwan, the proposed system can effectively be used to minimize the incorrectly classified and improve the performance of quality prediction. Finally, two machine learning algorithms (i.e., decision tree and Naive Bayes) was also used by [PERSON] et al. for automating the process of inspecting the quality of machine components [46]. Three types of machine component quality (i.e., good, minor scratch, and deep scratch) were measured. The results showed that the proposed method can effectively be used in automating the quality inspection of the product in real practical case. Fault detection and diagnosis is an important problem in process engineering and is utilized to detect abnormal events in a process. Early detection of process faults can help avoid productivity loss. Machine learning algorithms such as Random Forest showed significant efficacy in detecting process faults in manufacturing. Random Forest is an ensemble prediction method that aggregates the results of individual decision trees [66]. Generally, Random Forest works by utilizing the bagging method to generate subsets of training data. For each training dataset, a decision tree algorithm is utilized. In the end, the final prediction result is selected based on majority vote (the most voted class) over all the treesin the forest. Recently, Random Forest was used by [PERSON] et al. for detecting the failure of rotor bar. They performed the performance analysis between Random Forest and other models (i.e., decision tree, Naive Bayes, logistic regression, linear ridge, and support vector machine). The experimental results showed that Random Forest outperformed the other models and has around 98.8% of accuracy. The proposed model can be used for real-time fault monitoring system as well as the preventive maintenance system in factory. Random Forest also was utilized by [PERSON] and [PERSON] for detecting the failure of bearing [48]. The results were compared with those obtained from an existing artificial intelligence technique, neural network.