Energy Analysis During Milling Operation of Aerospace Alloys

Abstract

The world is trying to reduce its energy use across all sectors, but especially in manufacturing, because of the depletion of fossil resources and rising environmental concerns. Developed countries, with their substantial manufacturing sectors, have high energy needs. To reduce energy costs and minimize environmental concerns, energy-efficient manufacturing, and process optimization are thus necessary. Manufacturing relies heavily on milling machines, and the literature that has been published to date confirms that the energy consumption of CNC milling operations varies on several parameters, including feed rate, depth of cut, cutting speed, tool type, and workpiece material. This study aimed to lower the amount of energy used during the milling processes of Al 6061-T6. A specific cutting energy map was constructed, by multi-objective optimization, and statistical analysis. While burr formation and surface roughness on the workpiece were considered, the main goal of the study was to lower the specific cutting energy consumption. Surface roughness was shown to be highly influenced by the number of inserts, depth of cut, feed per tooth, and cutting speed, in that order, according to statistical analysis. Higher depth of cut values and lower values for the other parameters were suggested for improved surface finish. Cutting speed was the most important factor in terms of specific cutting energy, followed by feed per tooth, depth of cut, and number of inserts. Higher values were recommended for all four machining parameters to minimize specific cutting energy. Different characteristics affected the creation of burrs, highlighting the necessity of multi-objective optimization. xxiii It was revealed that cutting speed, together with the number of inserts, depth of cut, and feed per tooth, had the most influence on changing the Grey Relational Rank using grey relational analysis and modified Taguchi design of experiments. A complete factorial design of the experiment was used to create an energy map, which demonstrated that reduced specific cutting energy consumption was correlated with higher machining parameter values. Additional insights were obtained by microscopic inspection of the chips and cutting inserts. With the help of this specific cutting energy map technique, machinists may choose the best settings to minimize energy usage and carbon footprints during aluminum- Al 6061-T6 milling operations.