Detecting discharge status of small-hole EDM based on wavelet transform

Abstract

Discharge waveforms contain information representing the gap discharge status of an EDM process. The gap discharge status has a great influence on the machining performance including the machining efficiency, workpiece surface integrity, and tool wear rate in EDM processes. In order to identify the gap discharge status effectively, wavelet transform is used to analyze the discharge waveforms. A data acquisition and processing system based on DSP is developed for high-speed wavelet transforms and related calculations. The wavelet transform result shows that each EDM pulse can be classified by judging the approximation coefficients of the wavelet transform result. Experimental results demonstrate that the wavelet transform detection is capable of capturing the primary features of each single discharge pulse, which are usually unable to be discovered by conventional discharge detection methods such as the average gap voltage detection. By analyzing the local extreme values of approximation coefficients, the numbers of different pulses within a detection time period can be identified. The gap discharge status coefficient, which is a function of the numbers of different pulses, is then calculated and used as a feedback signal to an adaptive EDM process controller. A small-hole machining test demonstrates that, with the online adaptive controller based on the wavelet transform method, the machining efficiency and stability are improved significantly.