Multi-objective robust optimization of the sustainable helical milling process of the aluminum alloy Al 7075 using the augmented-enhanced normalized normal constraint method

Helical milling is an eco-friendly hole-making process considering energy economy, tool inventory reduction, tool life cycle increase, set-up and non-productive times reduction due to tools changes and better borehole quality. In order to achieve the best results in terms of the sustainable manufacturing aspects energy, quality, and productivity, the present paper proposes to optimize the multi-objective helical milling process of the aluminum alloy Al 7075. With consideration to sustainable objectives, the axial cutting force component, related to energy consumption, the total roundness, related to borehole geometrical quality, and the material removal rate, related to productivity, were taken into account. The process factors axial and tangential feed per tooth and cutting velocity were selected, besides the noise factor tool overhang length, allowing to optimize bias and variance of cutting force and roundness together with the deterministic response material removal rate. In order to achieve a complete exploitation of the Pareto frontier, the new multi-objective optimization method augmented enhanced normalized normal constraint method was proposed. It was obtained a set of Pareto optimal solutions for the mean square error of the axial cutting force, mean square error of the total roundness, and material removal rate, achieving the trade-off among energy, quality, and productivity. Therefore, different optimization scenarios were obtained, allowing to the experimenter the possibility of choice, guaranteeing a sustainable hole-making process. Furthermore, besides allowing the possibility of choosing different solutions, the TOPSIS decision-making approach was performed so that the best compromise solution was found among the Pareto optimal solutions. The helical milling of the aluminum alloy Al 7075 is presented as a green machining process. (C) 2017 Elsevier Ltd. All rights reserved.