QFD-based expert system for non-traditional machining processes selection
Expert Systems with Applications: An International Journal
A novel approach for ANFIS modelling based on full factorial design
Applied Soft Computing
Expert Systems with Applications: An International Journal
Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
A new approach to intelligent fault diagnosis of rotating machinery
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A modified gradient-based neuro-fuzzy learning algorithm and its convergence
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Adaptive neuro-fuzzy inference system for combined forecasts in a panel manufacturer
Expert Systems with Applications: An International Journal
Prediction of building energy needs in early stage of design by using ANFIS
Expert Systems with Applications: An International Journal
Fire detection model in Tibet based on grey-fuzzy neural network algorithm
Expert Systems with Applications: An International Journal
An approach based on ANFIS input selection and modeling for supplier selection problem
Expert Systems with Applications: An International Journal
Optimization of magnetic field assisted EDM using the continuous ACO algorithm
Applied Soft Computing
Hi-index | 12.06 |
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 of 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.