Prediction of machinability parameters in turning operation using interval type-2 fuzzy logic system based on semi-elliptic and trapezoidal membership functions

Predicting the behaviour of a manufacturing operation is always challenging. Predictive analytics plays a major role in tackling errors present in the data acquired during the manufacturing process. Data uncertainties are unavoidable; however, they need to be mapped appropriately for the effective implementation of suitable control schemes. In this work, an attempt is made to predict the machinability of α–β titanium alloy during turning operation using three cooling agents such as dry, liquid nitrogen, and carbon dioxide. Interval type-2 fuzzy logic system (IT2FLS) along with centre of sets type reduction is considered to handle uncertainties present during the turning operation. The computational complexity of IT2FLS is overcome by reducing it to type 1 fuzzy logic system using Mendel's first results. Simulation results of both IT2FLS and T1FLS are compared with semi-elliptic membership function and trapezoidal membership function. The results obtained validate the Mendel's statement by reflecting similar behaviour in both the fuzzy logic systems. The results also confirm that the predictions of machinability parameters in turning operation using SEMF are a preferred option.