Feasibility research on break-out detection using audio signal in drilling film cooling holes by EDM

The electrical discharge machining (EDM) process with inner and outer flush is effective for drilling film cooling holes on the blades of aeroengines and gas turbines. Due to the hollow structure of the blades, the break-out status needs to be accurately detected in order to control the position of tool electrode end to prevent the back strike. Currently, the break-out detection is mainly realized based on the change of electrical signals and tool electrode velocity. The previous research shows that the machining stability of the process can be improved by optimizing the outer flush. However, in this case the break-out status is difficult to be detected using the existing methods. In this research, a novel break-out detection method using audio signal is proposed. The audio signals of the process are firstly collected and analyzed through time and frequency domain methods to provide the basis for algorithm design. Then a filter of the audio signals is designed based on the comparison of original signals collected before and after break-out to reduce the impact of noise. And the feature extraction algorithms are designed to reflect the characteristics of break-out with less dimension. Next a recognition model using artificial neural network is established by the features of audio signals, which are classified as before or after break-out. The recognition model is validated by test data acquired by machining experiments and the results verified that the break-out detection using audio signal is feasible in drilling film cooling holes.