Forward and reverse mappings of electrical discharge machining process using adaptive network-based fuzzy inference system

Input–output relationships of an electrical discharge machining process have been established both in forward as well as reverse directions using adaptive network-based fuzzy inference system. Three input parameters, such as peak current, pulse-on-time and pulse-duty-factor, and two outputs, namely material removal rate and surface roughness have been considered for the said mappings. A batch mode of training has been adopted with the help of 1000 data for the developed adaptive network-based fuzzy inference system, which has been designed using linear (say triangular) and non-linear (bell-shaped) membership function distributions of the input variables, separately. The performances of the developed models have been tested for both forward and reverse mappings with the help of some test cases collected through the real experiments. Adaptive network-based fuzzy inference system is found to tackle the problems of forward and reverse mappings efficiently. The fuzzy inference system utilizing non-linear membership functions is seen to perform slightly better than that with linear membership functions for the input variables.