Optimization of bearing steel turning parameters under CuO and ZnO nanofluid-MQL using MCDM hybrid approach

Abstract Metal cutting industries are intended to enhance the sustainability of machining processes. Minimum quantity lubrication system and nanofluid observed as beneficial option in the view of ecofriendly metal cutting operations. The main aim of the present experimental study is to analyze the influence of CuO and ZnO nanofluids on minimum quality lubrication assisted bearing steel turning process parameters. The response parameters surface roughness and machining temperature were used. The multi criteria decision making hybrid approach utilized to optimize the process parameters. The hybrid approach includes the entropy weights method and weighted aggregated sum product assessment technique. Experimentation concluded that CuO nanofluid noted as the effective cooling condition on comparing with ZnO nanofluid. The response parameters surface roughness and machining temperature significantly minimized by 38% and 12% respectively under CuO nanofluid with minimum quantity lubrication cooling condition.