## 2 State of the Art
### Machine Learning Techniques Applied to Machining Optimization  
The following works explore the suitability of machine-learning techniques in depth, to alleviate the limitations of machining-datasets:  
* Regarding the reduction of the number of expensive experimental tests, [PERSON] et al. [15] demonstrated that some machine-learning algorithms, for example, Random Forest ensembles, can provide accurate prediction models for critical depth of cut-and-chatter frequency in milling operations with a smaller number of experimental tests than experimental or analytical models. * With respect to the extraction of useful information from imbalanced datasets, [PERSON] and [PERSON] [16] demonstrated that some of those techniques, such as ensembles, can overcome unbalanced data in an extensive experimental dataset. They validated this approach to unbalanced data through industrial breakage detection of multitooth tools in real industrial datasets, showing successful detection of 59 insert breakages from a total dataset of 30,000 mechanized crankshafts. * As for the reduction of the number of features without losing information, [PERSON] et al. [17] demonstrated that Multilayer Perceptrons can make reliable predictions of surface roughness for face-milling operations, following dataset dimensionality reduction. They reduced the number of accelerometers needed in this case for reliable machine process monitoring. [PERSON] and [PERSON] [18] proposed the use of a genetic algorithm with neural networks to identify the best set of inputs to provide accurate prediction models for surface quality in high-torque face milling operations, reducing the data-acquisition costs within industrial environments. * The capability of machine-learning techniques to complete missing attributes, due to sensor malfunction or data-transmission error has also been studied in a few works. [PERSON] et al. [19] demonstrated that Genetic Algorithms and Multilayer Perceptrons can complete damaged datasets in deep drilling operations of steel components to predict borehole roughness. Besides, machine-learning techniques have proved their capability to create especially designed visual models for real-time visual processing of many manufacturing processes. [PERSON] et al. [20] used different machine-learning algorithms, among which k-Nearest Neighbors, neural networks and decision trees, to model some outputs of industrial interest in pulsed laser micromachining of micro geometries, such as dimensional accuracy, surface roughness and material removal rate. They demonstrated reliable models of immediate industrial application by means of decision trees, which can process direct rules or 3D-Charts that optimize process parameters. * The suitability of machine-learning techniques to build reliable prediction models for different machining-process cutting outcomes has been widely demonstrated. [PERSON] et al. [21] proposed the use of Bayesian Networks for breakage detection of cutting tools in crankshafts machining. They showed the flexibility of these types of networks to extract process information of direct industrial use by means of inquiries forwarded to the Bayesian Network when evaluating tool wear in a discretized way: broken or normal. [PERSON] et al. [22] proposed a Bayesian inference method to evaluate tool life for end milling of AISI 1018 in a continuous way, instead of with a discretized number of levels. Along the same lines, but using Multilayer Perceptrons, [PERSON] et al. [23] described the use of MLP-based automatic image analysis for assessing tool wear. Their results promised a good correlation between the new methods and the commonly used optically measured VB index for the entire life range of the tools. The same two strategies (continuous or discretized output) have been proposed for the two main workpiece-related quality indicators: surface roughness and dimensional accuracy. [PERSON] et al. [17] built accurate Multilayer-Perceptron models for surface roughness prediction in cast-iron face-milling operations.