## 5 Discussion
### RQ1
The supervised models can be used to find insights from the data, and the subsequent use of prognostics [105] and forecasting [106] will make sure that the production process runs efficiently with minimal costs incurred for maintenance and reduce product quality degradation [107]. Supervised Learning is a machine learning paradigm for acquiring the input/output relationship information of a system based on a given set of paired input/output training samples. As the output is regarded as the label of the input data or the supervision, an input/output training sample is also called labelled training data, or supervised data. As commented from an interview with [PERSON], almost all the economic value created by neural networks has been through supervised learning, while unsupervised learning is going to be absolutely crucial. What has worked over the last ten years or so is also supervised learning. Moreover, the predictive maintenance problem through supervised learning methods can be formulated in two ways. The first is to formulate the PdM as classification problems to determine the probability of a part failing before n numbers of wafers (for example) are processed. The second is to formulate it as regression problem where one would predict the time remaining until the next failure is expected to occur. The most used is evident to be classification, especially for manufacturing machinery [108]. **RQ6. **  
This question aimed to classify the different types of best-performing machine learning methods in terms of: prediction accuracy; precision; recall; minimum variance and root-mean-squared and mean absolute error. They aim to be used in evaluation and validation studies for prediction of different smart manufacturing equipment degradation. The resulting charts show neural network methods as the leader, followed by random forests and SVMs. All of these show a growing trend in the last 5 years and significant successful applications in manufacturing. Deep neural networks were pointed out as more relevant application methods, highlighting LSTM as the most promising algorithm used for predictive maintenance applications in smart manufacturing. However, the popularity of neural networks has to face the need of large volumes of training data, and this has limited their use for industrial applications where representative, labelled data might be difficult or costly to acquire. The deep learning based methods were at the forefront, bringing greatly improved analysis and recognition abilities but, at the same time, slowing down the running speed, as these methods require a lot of computation [109]. With the rapid development of smart industry, people invest their maximum effort on reducing manpower by deploying vision sensors in each manufacturing line and empowering them with the ability of autonomous defect detection, which results in a hugely increased data size [110]. Moreover, the improved methods of regularization allow deeper neural networks also to potentially be trained with smaller amounts of labelled data, bringing industrial applications within reach. The two most important aspects of modern neural networks are regularization and data abstraction [40]. Therefore, it is also important to underline that in machine learning, there is something called the \"No Free Lunch\" theorem. It states that no one algorithm works best for every problem, and it is especially relevant for supervised learning (i.e., predictive modelling) [111]. There are many factors at play, such as the size, quality, and nature of data; the available computational time; the urgency of the task; and what you want to do with the data [112]. As a result, many different algorithms should be tailored for each problem, while using a hold-out \"test set\" of data to evaluate performance and select the winner. Of course, the algorithms tried must be appropriate for the selected problem, which is where picking the right machine learning task comes in.