# Recent Advances on Machine Learning Applications in
## 1 Introduction  
The Fourth Industrial Revolution has enhanced the application of Machine Learning (ML), improving the machining capabilities and reducing the production costs. Articles on ML in machining have been written from the late 1980s-1990s in the industry, as, for example, by [PERSON] et al. [1], where a Decision Tree algorithm was applied for performance monitoring in plunge grinding based on vibration signals with different frequency domain features. [PERSON] et al. [2] used an Artificial Neural Network (ANN) for predicting optimal operating conditions (MRR maximization) in turning using process parameters as the input. [PERSON] et al. [3] in 1995 used an ANN based on time domain features from acoustic emission, vibration, cutting force and time signals in milling for surface roughness and bore tolerance prediction. These were some of the first articles that showed the application of ML in machining; nevertheless, these were sparse and limited due to the low computational capacity in their times. Stochastic-based models have been implemented in the literature for machining applications [4; 5; 6]. The well-known distributions, such as Gaussian, Log-normal, Exponential and Weibull, are the mathematical techniques applied as the lives of tools are explored, and the optimization of the parameters is requested [7]. These models are based on the inner-relations searches between the tool wear factor and process parameters or surface roughness, with the aim of optimizing the tool life. The proportional hazards model developed by [PERSON] may be used for fog model optimization [8]. Other models differ on the process parameters and signal features, and an optimization step is required for Cox and the selected distribution parameters [9]. The conventional methods in the machining context are commonly established on a physical model with the aim to extrapolate correlations between the model variables. Meanwhile, the ML model's purpose is to obtain an accurate as possible predictor that is frequently difficult to recognize with the physical term. Conventional methods require several assumptions, such as the statistical distribution model for the studied variables. ML methods enable to consider the available information without screening or prioritization actions, obtaining a superior degree of flexibility [10]. Moreover, a significant expansion of ML started from 2015 with an intensive application of methods and algorithms. For this reason, this review aims to evaluate the advances of ML focusing on the recent state-of-the-art innovations, analyzing the main techniques and the obtained results. ML architectures may be classified into supervised, semi-supervised and unsupervised learning. Supervised learning applications are composed by processes where there are known input and output dataset values, and the forecast outcomes of the models need to be verified with the real resultant dataset. An unsupervised dataset is arranged by input information without knowledge of the resultant output. Finally, semi-supervised learning uses mix the known features and unknown dataset. This learning method has been implemented to improve the time-consuming limitations and corresponding expenses in supervised learning. The combination of previously mentioned architectures shows a number of challenges due to the uncertainty of the unknown result dataset [11]. The ML approach is applied in machining to solve a broad range of issues and related root causes, such as the poor quality of the production due to process vibrations, thermal conditions and component breakages or a low efficiency of equipment due to ineffective monitoring of the component life, incorrect maintenance scheduling and high energy consumption of a machine tool. Although these types of problems may seem different, they can be addressed using the same approach.