Sustainable machining. Modeling and optimization of temperature and surface roughness in the milling of AISI D2 steel

Abstract

PurposeSustainable machining is a global consensus and the necessity to cope up the serious environmental threats. Minimum quantity lubrication (MQL) and nanofluids-based MQL(NFMQL) are state-of-the-art sustainable lubrication modes. The purpose of this study is to investigate the effect of process parameters, such as feed rate, depth of cut and cutting fluid flow rate, on temperature and surface roughness of the manufactured pieces during face milling of the AISI D2 steel.Design/methodology/approachA statistical technique called response surface methodology with Box–Behnken Design was used to design experimental runs, and empirical modeling was presented. Analysis of variance was carried out to evaluate the model’s accuracy and the validation of the applied technique.FindingsA comprehensive analysis revealed the superiority of implementing NFMQL in comparison to MQL within the levels of process parameters. The comparison has shown a significant reduction of temperature under NFMQL at the tool-workpiece interface from 16.2 to 34.5 per cent and surface roughness from 11.3 to 12 per cent.Practical implicationsThis research is useful for practitioners to predict the responses in workshop and select appropriate cutting parameters. Moreover, this research will be helpful to reduce the resource which will ultimately save energy consumption and cost.Originality/valueTo cope with the industrial challenges and tribological issues associated with the milling of AISI D2 steel, experiments were conducted in a distinct machining mode with innovative cooling/lubrication. Until now, few studies have addressed the key lubrication effects of Al2O3-based nanofluid on the machinability of D2 steel under NFMQL lubrication condition.