A pulse classification technique for monitoring the type of discharges in an electrochemical discharge machining (ECDM) process is presented in this research paper. The performance of an ECDM process is affected by many factors which make it hard for control strategies to be formulated for this process. The pulse classifier plays an important role to develop control strategies and later to improve the process. The proposed system uses the current and voltage waveforms measured through the gap as input signals for the classification system. A fuzzy inference system (FIS) is used to categorize both input signals into one of the four proposed pulse types, according to their specific behavior. For the experimental validation, data samples taken during the machining process were recorded to evaluate the performance of the pulse classifier with raw data. Raw data of the gap signals is properly classified based on the proposed FIS.

本文介绍了一种用于监测电化学放电加工（ECDM）过程中放电类型的脉冲分类技术。ECDM过程的性能受许多因素影响，这使得很难为该过程制定控制策略。脉冲分类器在开发控制策略以及随后改善过程方面起着重要作用。提出的系统使用通过间隙测量的电流和电压波形作为分类系统的输入信号。根据输入信号的特定行为，使用模糊推理系统（FIS）将两个输入信号分类为四种建议的脉冲类型之一。为了进行实验验证，记录了在加工过程中采集的数据样本，以利用原始数据评估脉冲分类器的性能。