ANFIS based forecast and parametric investigation during processing activity of AA6082T6

Abstract

ABSTRACT The paper describes a study that identifies the influence of the machining parameter on the temperature and the surface roughness for the end milling of AA6082T6 under dry cutting conditions. The experiments are based on Taguchi L9 DOE and ANFIS (Adaptive Neuro-fuzzy information system) is applied to determine optimal parameters. The consequences of period boundaries on execution have been explored with the aid of an effective plot. It was obtained that speed is the dominant aspect of the TEMP influencing the boundary for shifting (parametric commitment is 91.336%), while if surface roughness exists, the speed limit is the most contributing boundary (parametric commitment is 50.174%). Besides, to understand and set up the data yield relationship, the ANFIS-based show was carried out. The experimental results, ANFIS, and the anticipated results of artificial neural network (ANN) were analyzed and finally, it was found that the anticipated results of ANFIS are correct for anticipating the reactions during the AA6082T6 milling operation.