Prediction of Responses in a Sustainable Dry Turning Operation: A Comparative Analysis

In a turning operation, involving removal of material from the outer diameter rotating cylindrical workpiece using single-point cutting tool, there exist complex relationships between various parameters and responses. this paper, operation under dry environment is considered with speed, feed rate, depth cut as input parameters, well average surface roughness, force Dry reduces energy consumption machining cost, thus eventually resulting in sustainable machining. For process, corresponding response values are envisaged four prediction models, that is, multivariate regression analysis, fuzzy logic, artificial neural network, adaptive neurofuzzy inference system (ANFIS), their performance contrasted five statistical metrics, root mean squared percent error, absolute percentage log correlation coefficient, relative error. It noticed ANFIS model consisting advantages features both logic network outperforms other models respect to computed measures. Based on acceptable values, it can be propounded effectively employed for process responses while treating different variables.