Tool wear and material removal predictions in micro-EDM drilling: Advantages of data-driven approaches

Abstract

In micro electrical discharge drilling, regression models are commonly used for predicting the material removal rate (MRR) and tool wear rate (TWR) from the applied processing parameters. However, these models can be inaccurate since the processing parameters might not always be representative of the actual machining conditions, which depend on several other factors such as the tool length or gap flushing efficiency. In order to increase the prediction accuracy, the present work investigates the capability of data-driven regression models for tool wear and material removal prediction. The errors in predicting the MRR and TWR are shown to decrease of about 65% and 85% respectively when using data collected through process monitoring as input of the regression models. Data-driven approaches for in-process tool wear prediction have also been implemented in drilling experiments, demonstrating that a more accurate control of the hole depth (50% average reduction of the depth error) can be achieved by using data-driven predictive models.