## 5 Results and Discussion
### Prediction Accuracy  
The predictions of the MRR and TWR for the three predictive models are shown in Figures 6 and 7 respectively. When comparing the performance of the DD or DD + PP models with that of the PP model, an improved pattern can be seen. This suggests that, when including the data collected through process monitoring, the prediction is more accurate than when only considering the processing parameters. This is because many factors influence the material removal process besides the set of processing parameters, which is given as input by the machine user. An example is the flow field generated by dielectric flushing, which determines the temperature, pressure and velocity distributions within the discharge gap [30; 31; 32]. The effects of the flow field have been illustrated and analysed by physical-based models and calculated by finite element methods [30; 33; 34], showing to have a significant impact on the material removal process by electric discharges. The combination of thermal and electric action in material removal also influences the discharging process [35]. The improved prediction when using data collected during the drilling process shows that data-driven predictive models are capable of considering the effects of other influencing factors besides the processing parameters.  
Figure 6: Prediction of material removal rate (MRR) with different regression models.  
Figure 7: Prediction of tool wear rate (TWR) with different regression models.  
In order to better quantify the prediction accuracy of the three predictive models, the squared difference (SD) for the ten experimental runs can be analysed (Table 5). It can be noticed that the SD values for the DD and PP + DD models are significantly lower than the ones for the PP model, both for the MRR and TWR predictions. In particular, the reduction in the average of the SD values range between 85% and 95%. The standard deviations of the SD values are also considerably smaller for the DD and PP + DD models, indicating a reduction of the maximum deviation between the actual and predicted values of the MRR and TWR. This further proves that data collected during the drilling process can reduce the prediction error of the regression models.  
The NRMSE errors for the three regression models are listed in Table 6. For material removal prediction, the NRMSE is decreased of about 69% and 63% when the DD and DD + PP models are respectively used instead of the PP model. For tool wear prediction, the reductions in the NRMSE error are about 82% and 89% respectively. It can be noticed that the DD model is the model performing better when predicting the MRR, while the DD + PP model outperforms the other two models when predicting the TWR. However, the values of the NRMSE errors for the DD and DD + PP models are very close in both cases. If taking into account that the prediction accuracy of the two models is evaluated by considering a very limited number of experimental runs, it is difficult to state which of the two models is actually performing better. Anyway, the advantages of data-driven approaches are clear. Future research work should focus on evaluation of the prediction accuracy of the DD and DD + PP models using a more extensive dataset. The use of data-driven approaches for regression models could also be advantageous for monitoring, predicting and controlling the drilling process in real-time. This is discussed in the following section.