With increased production and productivity in modern industry, particularly in the automotive, aeronautical, agro-food, and other sectors, the consumption of manufacturing energy is rapidly increasing, posing additional precautions and large investments to industries to reduce energy consumption at the manufacturing system level. This research proposes a novel energy optimisation using a response surface methodology (RSM) with artificial neural network (ANN) for machining processes that saves

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... ergy while improving productivity.The feed rate was discovered to be the most influential factor in this study, accounting for 84.13 percent of total energy consumed. Furthermore, it has been established that as the material removal rate (MRR) increases, energy efficiency (EE) declines. This optimization of cutting conditions gives us the optimal values of cutting speed Vc = 129.37 m/min, feed rate f = 0.098 mm/rev and depth of cut ap = 0.5 mm. This approach will allow us to decrease the total energy consumed (Etc) by 49.74 % and increase the energy efficiency (EE) by 13.63 %.