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.

高速放电加工（EDM）是一种非传统的加工方法，使用高电能来有效地去除材料。本文提出了一种基于递归神经网络（RNN）的脉冲分类方法，用于高速EDM脉冲分析。这项研究是首次将RNN应用到高速EDM脉冲分析中。与传统的电火花加工不同，高速电火花加工的放电脉冲在加工过程中分为五类：断路，火花，电弧，部分短路和短路。建立了基于三种具有不同激活函数的RNN的模型，包括传统的RNN，LSTM（长短期记忆）和IndRNN（独立的递归神经网络），以分析研究中的放电脉冲。在分类方法中提出了一种基于最小信号变化周期的新输入数据结构，以简化模型结构并同时提高精度。在不设置阈值的情况下，所提出模型的最高分类精度高达97.85％，可以同时基于10,000个数量级（包括各种电流值）对放电脉冲进行分类。该方法有效地适应了复杂的加工条件和高速电火花加工的复合动力源。使用最佳模型分析了在不同电流，通量和进给速度下加工过程中放电脉冲的分布。可以清楚地预测出放电脉冲的比例。通过分析较长加工时间的放电脉冲，