Optimization of sustainable milling of SKD11 steel under minimum quantity lubrication

Abstract

In this study, the sustainably face milling of SKD11 steel under a minimum quantity lubrication condition was investigated. The goal of this work is twofold: (i) observing the impact of main milling parameters, including cutting speed ( Vc), depth of cut ( ap), and feed per tooth ( fz) on surface roughness ( Ra) and specific cutting energy (SCE), and (ii) finding out an optimal combination of cutting parameters for ameliorating the surface quality, productivity, and diminishing energy consumption. For this purpose, the experiment was designed by using the Box–Behnken matrix. A series of 15 experimental runs have been conducted to acquire experimental data. The regression models of Ra and SCE were subsequently developed and evaluated by using the analysis of variance. Finally, the multi-objective optimization issue was resolved by using the technique for order of preference by similarity to ideal solution (TOPSIS) algorithm and the desirability approach (DA). The results show that the parameter fz reveals the highest impact on surface roughness and SCE, followed by ap and Vc. The TOPSIS method allows finding the optimal solution with a reduction of 46% in terms of SCE when compared with that achieved by DA. This is significant in the context of sustainable manufacturing. This research is expected to enrich the knowledge of milling SKD11, and the achieved results can be used for research community and manufacturing industries.