Micro-ECDM Performances Analysis Using Fuzzy Logic and ANFIS During Micro-Channel Fabrication on Silica Glass

Abstract

Higher accuracy and meticulousness are highly demandable in modern industrial field during micro-machining performances by electrochemical discharge machining (ECDM) process. The paper deals with the experimental fuzzy logic control (FLC) analysis as well as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) analysis during micro-channel fabrication on silica glass. A comparative analysis of FLC, ANN as well as ANFIS has been performed and experimental error prediction has been propounded to estimate minimum error possibilities such as mean absolute error (MAE), root mean square error (RMSE) and regression value (R). Computational time training dataset, minimum sample size and prediction time are also illustrated in this paper. In this paper, ANN, FLC and ANFIS models are analyzed for tool wear rate (TWR) and heat-affected zone (HAZ). 3D Rule Viewer and regression analysis as well as validation of test results and characteristics graph between performances with number of epochs of ANN model also are included in this paper for TWR and HAZ. Influence of process parameters like voltage, duty ratio, pulse frequency and electrolyte concentration on Surface Viewer of TWR and HAZ is also illustrated. It is found that ANFIS has great effectiveness of prediction of error during micro-ECDM process.