Open AccessA NOVEL METHOD FOR SELECTING THE OPTIMAL EDM PROCESS FOR HASTELLOY B2 USING THE MODIFIED-ADDITIVE RATIO ASSESSMENT METHOD (M-ARAS) BASED ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)
Author(s) -
Ramasubbu Narasimmalu,
S. Ramabalan
Publication year - 2021
Publication title -
materiali in tehnologije
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.27
H-Index - 25
eISSN - 1580-3414
pISSN - 1580-2949
DOI - 10.17222/mit.2021.144
Subject(s) - adaptive neuro fuzzy inference system , duty cycle , materials science , electrical discharge machining , fuzzy inference , process (computing) , nonlinear system , machining , mathematical optimization , fuzzy logic , control theory (sociology) , voltage , computer science , metallurgy , fuzzy control system , mathematics , artificial intelligence , engineering , physics , control (management) , quantum mechanics , electrical engineering , operating system
Electrode wear and metal removal exhibited nonlinear behavior in the Electrical Discharge Machining (EDM) of Hastelloy B2 plate. Hence, mathematical modeling was used to solve this problem. The hole size, pulse duration, duty cycle, and current were selected as inputs. Squareness and taper angle were considered as responses. Therefore, the Modified-Additive Ratio Assessment Method (M-ARAS) based Adaptive Neuro Fuzzy Inference System (ANFIS) method was used to find the optimum EDM process parameters. The overall analysis showed that the M-ARAS-based ANFIS algorithm provided a good fit for optimization of the process parameters and could be used for further multi-objective optimization problems.