## 1 Introduction  
Micro electrical discharge machining (micro-EDM) drilling is a thermal process in which material removal occurs through electric discharges sequenced at high-frequency. Thanks to its non-contact nature, micro-EDM drilling allows the machining of any conductive material regardless of its hardness or strength. For this reason, it is widely applied in industry for making small holes in difficult-to-machine materials such as titanium alloys, tungsten carbide and nickel superalloys [1; 2; 3]. Typical applications in the automotive and aerospace sectors include spray holes for diesel injector nozzles [4] and cooling holes for turbine blades [5]. Due to the nature of electric discharges, in micro-EDM drilling material is inevitably not only removed from the workpiece, but also from the tool electrode. This results in significant tool wear, which can be of the same order of magnitude as the amount of material removed from the workpiece when using brass or copper tubes as drilling tool [6; 7]. Therefore, methodologies for predicting the wear of the tool electrode as well as of the material removal on the workpiece side are needed for accurately controlling the depth of a micro-EDMed hole. Traditionally, regression models have been used for predicting what is commonly denoted as the material removal rate (MRR) and tool wear rate (TWR) from the setting of micro-EDM processing parameters. [PERSON] et al. [8] used a regression model for predicting the TWR in micro-EDM drilling of silicon carbide (SiSiC) considering three processing parameters as input (pulse-ontime, voltage and current). [PERSON] et al. [9] developed a procedure using a multiple linear regression model for predicting several machining characteristics, including the MRR and TWR. [PERSON] et al. [10] established a non-linear regression model for predicting the TWR and MRR when machining aluminium matrix composites. Methods different from regression models have also been tried. For instance, [PERSON] et al. [11] proposed a combination of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) methods for predicting the MRR and TWR from the applied setting of processing parameters. [PERSON] et al. [12] applied a response surface methodology (RSM) to predict the MRR and TWR in EDM of tool steel, considering the pulse-on time, current, and voltage as input processing parameters. In all above-mentioned methods the processing parameters are used to predict the tool wear and material removal. However, the processing parameters might not always be extremely representative of the actual machining conditions, which depend on several other factors such as the tool length or gap flushing efficiency [13; 14]. Meaningful information about the actual machining conditions could be obtained through process monitoring, which involves the use of sensors for monitoring and recording physical quantities during the machining process. Extensive sets of data are generated through process monitoring. These data could be used in addition or even in replacement of the set processing parameters to build regression models in order to obtain a more accurate prediction of the MRR and TWR. Previous research work has already proved that a good degree of correlation can be established between the main material removal performance indicators (MRR or TWR) and specific physical quantities that can be monitored during the micro-EDM process. [PERSON] et al. [15] have shown that a linear correlation exists between the average tool wear per discharge and the average energy per electric discharge, which can be computed by monitoring the current and gap voltage signals. Similarly, [PERSON] and [PERSON] [16] have demonstrated that the average discharge energy is representative of the machining conditions and can be correlated to the drilling time and tool wear.