# Recent Advances on Machine Learning Applications in
## 2 Machine Learning Paradigm in Machining Application
### Tool Condition Monitoring  
The modeling and prediction of the tools' condition may offer significant opportunities in increasing the MT performance level, since it may ensure the quality of the parts, improve machining efficiency and reduce the operation costs. For this reason, several ML techniques may be evaluated and applied to model this phenomenon. The wear evolution may classify and be discretized in three main phases. The first phase presents fast wear of the tool, usually at the beginning of the process when the friction between the tool and the workpiece generates the maximum stress. In the second phase, the tool and speed wear are steady, since the roughness of the tool achieves a certain smoothness. Finally, the cutting force arises due to the blunt tool edge, and the wear speed increases with the resulting temperature, causing heat deformation, precision errors and, finally, the tool breakage. The literature shows two main ML techniques to address this problem based on direct and indirect methods. Direct methods aim to directly measure the wear on the tool; this could also be achieved through the application of ML from images to avoid the corresponding time needed for the manual measurement. [PERSON] et al. [31] developed a CNN approach based on tool images focusing on the spindle speed in order to allow the camera to detect the effective images. The results indicated an average recognition precision rate of 96.20% in tool wear classification (flank wear, tool breakage, adhesive wear and rake face wear). Other applications are required to stop the machining process for the tool wear measurement [32; 104], with significant limits in terms of time waste. Despite the high model accuracy and precision of direct methods, the request to stop the MT generates several issues in production. Moreover, the direct measure requires a high knowledge of the process to define when the tool wear should be measured, increasing the complexity of the sample method. In contrast, the indirect methods are based on sensor signals (e.g., vibrations, acoustic emission or forces) and are applied to conduct continuous or intermittent estimations and predictions of the tool wear. The implementation of indirect methods, despite a reduction in the final accuracy, may be preferred and provide new opportunities to develop a TCM application. The indirect methods are based on two main approaches: classification, that may vary from multiple classes to a binary application, and regression, to obtain the current wear status or the RUL. [PERSON] et al. [23] developed a TCM classification in six wear states: good, slight, average, heavy, severe and failure. The authors obtained the wavelet packages from the sound signal in order to separate the source signals from the wavelet sub-bands. They applied the Extended Convolutive Bounded Component Analysis (ECBCA), and finally, they decomposed the source signals into time-varying oscillatory components using the Multivariate Synchro squeezing Transform (MSST) for denoising. The Adaptive Kernel Principal Component Analysis (AKPCA) was implemented to decompose the extracted features in a set of linearly uncorrelated components to obtain an input vector that allowed the application of a simpler ML method, such as SVM with an RBF kernel with a testing accuracy of 98.47%. Simpler ML applications need extra signal treatment and feature extraction for correct functioning. For example, a Two-Layer Angle Kernel Extreme Learning Machine (TAKELM) was found in three different applications by changing the input signals: sound [105], current [106] and multi-sensor [107], where a Binary Differential Evolution (DBE) was applied to search the optimal feature parameter combination. In order to avoid any extra sensors mounted on the machining center, [PERSON] et al. [108] presented an indirect measurement system using HSV images of the produced chips, obtaining good performances on medium and high wear, but they obtained poor performances in initial wear recognition.