Pulse Classification for an Electrochemical Discharge Machining Process Based on Fuzzy Logic Approach

Abstract

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.