# Recent Advances on Machine Learning Applications in
## 3 Discussion
Considering indirect methods, which are the ones that allow a continuous functioning of the machining center and a real-time control of tool wear and RUL estimation, a correct choice would be to deploy a DL strategy, through SSAE, LSTM (or RNN) and CNN, for feature extraction, which would allow an extra degree of freedom for the choice of the actual modeling algorithm, which may even be a ML one. If the available dataset is small, a good choice is to aim at using a simpler ML algorithm as ELM or SVM, both of which obtained good results when the lack of data was one of the issues. If the amount of data available is not an issue, a DL application should be deployed, as this type of configuration allows for less interference of human choices and a greater generalization of the algorithm scheme. The application of CNN is particularly interesting for feature extraction, together with an LSTM for temporal feature recognition. The physical comprehension of the model and the amount of data expected are two factors that require an extensive experience in ML strategy deployment. White-box models (e.g., DT and RT) are based on patterns or rules that can be understood by experts and/or technicians. Black-box models (e.g., CNN and DNN) contain complex mathematical functions, and their deployment is not determined by predictable interactions [115]. The tradeoff between model complexity and depth affects the computation time of the training phase. A concern regarding the model depth is the double-descent phenomena, where the performance varies when the model complexity increases or the number of training epochs is altered, as was investigated in Reference [116]. The selection and installation of the sensors may play a critical role in the ML results. Depending on the application, the use of the sound sensors (microphones) and cameras may be suitable, although their implementation in an actual industrial environment may find several drawbacks. For example, cameras, apart from being an expensive sensor, need a correct illumination, positioning scheme and a clean environment (dust may influence the measurement), and even floor vibrations may influence the final image quality. Moreover, the actual image quality will result in high-dimensional input data, which means more complex algorithms are needed in order to be used; for CNN applications, although the image could be used, a first filtering and data reduction phase should be implemented in order to reduce the computational time required. Sound sensors instead, despite their great ability in grinding wheel-worn control and chatter recognition, require an important phase of signal treatment typically developed for each application, and the actual microphone disposition may generate several issues in the actual practical application. The advantage of these last sensors is that they do not influence the actual machining center or change their configuration, as happens, for example, with the implementation of a dynamometer. This type of sensor may limit the dimensions of the workpieces; moreover, they are expensive and interfere with the rigidity of milling machines. Although sensor positioning is crucial for the measurements, vibration sensors tend to usually be applied among different applications, such as for sound sensors; there is the need for an extensive signal processing phase for this kind of sensor, as they are difficult to filter, and vibrations exist even during air-cut operations. AE sensors have a superior sensitivity of high-frequency signals but are highly sensitive to environmental noise, and it is difficult to process due to intermittent cutting, such as, for example, in milling processes, there is a spike when each individual flute enters or exits the workpiece. Due to the nature of the manufacturing process, signals are usually nonstationary and contain both high- and low-frequency components, which make the application deployment with single sensors more difficult.