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Go to Editorial ManagerThe present work demonstrates the optimization process of Micro- hole of Electrical Discharge Machining (EDM) by Adaptive Neuro Fuzzy Inference System (ANFIS). The workpiece material was copper alloy. The current, gap distance and pulse on time were the control parameters of EDM. The process has been successfully modeled using ANFIS model constructs a fuzzy inference system in MATLAB 7.2 Software Gaussian type for optimization of micro diameter, were adopted during the training and testing process of ANFIS model in order to compare the prediction accuracy of micro diameter by one membership function. Finally, the comparison of ANFIS results with experimental data indicates that adoption of Gaussian membership function in proposed system achieved satisfactory accuracy. Prediction using ANFIS model compared with experimental values of micro holes at correspond ratio 98.37%.
This study implements the soft computing techniques such as Artificial Neural Network (ANN) and an adaptive Neuro-Fuzzy (ANFIS) approach. Thus to model the rutting prediction with the aid of experimental uniaxial creep test results for asphalt mixtures. Marshall samples, having Maximum Nominal Size of 12.5 mm, have been selected from previous studies. These samples have been prepared and tested under different conditions. They were also subjected to different loading stress (0.034, 0.069, 0.103) MPa, and tested at various temperature (10, 20, 40, and 55) °C. The modeling analysis revealed that both approaches are powerful tools for modeling creep behavior of pavement mixture in terms of Root Mean Square Error and Correlation Coefficient. The best results are obtained with the ANFIS model.