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Intelligent hybrid approaches for ensuring better prediction of gas-assisted EDM responses
SN Applied Sciences
The present research work explores the implementation of three smart hybrid predictive models based on the adaptive neuro-fuzzy inference system (ANFIS), ANFIS and genetic algorithm (GA), and ANFIS and particle swarm optimization (PSO). All such strategies have been used to determine and compare machining key elements including material removal rate (MRR) and surface roughness (SR) during the gas-assisted electrical discharge (GAEDM) process. In this study, inert gas-based EDM with a multi-holedoi:10.1007/s42452-020-2654-y fatcat:znblag46k5f4tdzharnbzrpri4