Machine tools have an impact on the environment due to their energy consumption. New strategies with focus on the reduction of the energy consumed by manufacturing processes have received significant attention owing to the rise of the electricity costs. This paper presents an experimental study related to the optimization of cutting parameters in turning of AISI 1018 steel. The aim of the study was to minimize the quantity of electrical energy required by the machine tool in order to perform the cutting operation. The material removal rate was set to a constant value in all the experimental trials so as to analyze the effect that the cutting parameters have on the energy consumed. Robust Design was used to determine the effects of the depth of cut, feed rate, and cutting speed on the energy required by the machine tool, considering two sources of noise in the experimental trials. The results of this work show that the techniques covered by the concept of Robust Design can be used to minimize the energy consumed and variation of the machining process.
Minimum Quantity Lubrication or MQL is an increasingly used technique for metal cutting operations and it has become an attractive alternative for machining parts at big scale production. However, fully lubricated conditions are still in use for machining special materials so that surface finish, tool wear, and temperature distribution levels remain at optimum levels. On the other hand, dry condition machining is in use as well although with some restraint due to issues with material burr, surface roughness, and tool wear. The main purpose of this work is to analyze the effects of cutting fluid flow rate, its application mechanisms, and cutting speed on surface roughness and establish the lowest possible cutting fluid flow rate that yields to minimum surface roughness (Ra). To achieve the objective, a set of experiments was performed using a Computer Numerical Control (CNC) lathe instrumented with a Kistler 9121 dynamometer and a customized cutting fluid application system to obtain coefficients of friction and cutting forces. Finally, a previously 2D finite element analysis (FEA) simulation from Akbar et al. [1] is applied and compared to experimental results to find out if the cutting force can be predicted. A first regression model that correlates cutting force and surface roughness is posed, so that FEA simulation can be implemented to predict the final surface roughness. AISI 4140 machinery steel in annealed condition is used to carry out the simulated and experimental work.
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