An energy-efficient optimization of the hard turning using rotary tool

The turning operation using a self-propelled rotary tool (SPRT) is efficient manufacturing for hard machining. However, optimization-based energy saving of the rotary turning has not presented because of expensive implementation. This study addresses a parameter optimization to enhance the machining rate (MR) and decrease the energy consumption (ET) as well as the machined roughness ( R ) for a hard turning using SPRT. The process inputs are the inclined angle ( α ), depth of cut ( a ), feed rate ( f ), and cutting speed ( V ). The hard turning runs were performed using the experimental plan generated by the Taguchi approach. The adaptive neuro-fuzzy inference system (ANFIS) was used to construct the correlations between the process inputs and responses. The analytic hierarchy process technique was adopted to explore the weight values of the outputs, and the optimum solution was obtained utilizing the adaptive simulated annealing. Moreover, an integrative approach using the response surface method and utilizing the desirability approach was employed to select the optimal outcomes and compare with the proposed technique. The findings revealed that the proposed ANFIS models minimize the predictive error in comparison with the traditional one. The accurate weights may help to select reliable optimal results. The optimal values of the α , a , f , and V are 18°, 0.15 mm, 0.40 mm/rev, and 200 mm/min, respectively. Moreover, ET and roughness are decreased by 50.29% and 19.77%, while the MR is enhanced by 33.16%, respectively, as compared to the general process.