Intelligent pulse analysis of high-speed electrical discharge machining using different RNNs

Abstract

High-speed electrical discharge machining (EDM) is a nontraditional machining method using high electric energy to efficiently remove materials. In this paper, a novel pulse classification method was proposed based on the recurrent neural network (RNN) for high-speed EDM pulse analysis. This study is the first time that an RNN has been applied in high-speed EDM pulse analysis. Different from traditional EDM, discharge pulses of high-speed EDM were classified into five types during the machining process: open, spark, arc, partially short and short. Models based on three different RNNs including the traditional RNN, LSTM (long short-term memory) and IndRNN (independently recurrent neural network) with different activation functions were built to analyze the discharge pulses in the research. A new input data structure based on the minimum signal change period was proposed in the classification method to simplify the model structure and improve accuracy at the same time. Without setting thresholds, the highest classification accuracy of the proposed model is up to 97.85%, which can simultaneously classify discharge pulses based on 10,000 orders of magnitude including various current values. The proposed method was effectively adapted to the complicated machining conditions and the compound power source of the high-speed EDM. The optimal model was used to analyze the distribution of discharge pulses during the machining process under different currents, fluxes and feeding speeds. The proportion of the discharge pulses was clearly predicted. Through analyzing the discharge pulses of long machining time, the regulation of discharge under different machining parameters was revealed more reliably, providing valuable information for the improvement of high-speed EDM servo systems.