An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM - Caydas, Ulas; Hascalik, Ahmet; Ekici, Sami (Pergamon-Elsevier Science Ltd, 2009)
A wire electrical discharge machined (WEDM) surface is characterized by its roughness and metallographic properties. Surface roughness and white layer thickness (WLT) are the main indicators of quality of a component for WEDM. In this paper an adaptive neuro-fuzzy inference system (ANFIS) model has been developed for the prediction of the white layer thickness (WLT) and the average surface roughness achieved as a function of the process parameters. Pulse duration, open circuit voltage, dielectric flushing pressure and wire feed rate were taken as model's input features. The model combined modeling function of fuzzy inference with the learning ability or artificial neural network; and a set of rules has been generated directly from the experimental data. The model's predictions were compared with experimental results for verifying the approach. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords: Wire-EDM; Fuzzy inference; Artificial neural network; Artificial intelligence techniques