In this paper, a rotary tool with rotary magnetic field has been used to better flushing of the debris from the machining zone in electrical discharge machining (EDM) process. Two adaptive neuro-fuzzy inference system (ANFIS) models have been designed to correlate the EDM parameters to material removal rate (MRR) and surface roughness (SR) using the data generated based on experimental observations. Then continuous ant colony optimization (CACO) technique has been used to select the best process parameters for maximum MRR and specified SR. Here, the process parameters are magnetic field intensity, rotational speed and product of current and pulse on-time. Also, ANFIS models of MRR and SR are the objective and constraint functions for CACO, respectively. Experimental trials divided into three main regimes of low energy, the middle energy and the high energy. Results showed that the CACO technique which used the ANFIS models as objective and constrain functions can successfully optimize the input conditions of the magnetic field assisted rotary EDM process. 
Optimization of magnetic field assisted EDM using the continuous ACO algorithm