RESUMEN
Developing sustainable manufacturing methods that balance environmental and economic aspects is challenging. A comprehensive analysis of the economics of machining and carbon emissions is essential to encourage adopting sustainable practices. This work presents the machinability and comparative sustainability analysis of Nimonic 80 superalloy when it is machined utilizing a novel, environmentally friendly vegetable oil-based hybrid nanofluid-minimum quantity lubrication (MQL) and liquid carbon dioxide (LCO2) technique. The main objective is to comprehend the efficacy of the proposed approach on tool life, surface roughness, power consumption, total machining costs, and carbon emissions. Compared to other machining conditions, the use of hybrid nanofluid-MQL under 100 m/min cutting speed prevented rapid flank wear and considerably increased tool life by about 17-59 %. The change in cutting speed from 100 to 150 m/min has resulted in reduced tool life about 13-42 % under the selected environments. In addition, when compared to dry, flood, and MQL machining, the use of hybrid nanofluid-MQL and LCO2 reduced surface roughness by around 16-45 % at 150 m/min. Sustainability analysis revealed that machining at 150 m/min resulted in decreased costs ranging from 6.1 % to 36.4 % for selected cutting environments. Applying hybrid nanofluid-MQL lowered carbon emissions by 16.83 %, whereas LCO2 reduced carbon emissions by 14.6 % at 100 m/min. At 150 m/min, hybrid nanofluid-MQL and LCO2 lowered carbon emission by 22.3 % and 21.5 % at 150 m/min compared to dry machining. Compared to alternative cutting environments, hybrid nanofluid-MQL and LCO2 applications have longer tool lives, lower machining costs, and carbon emissions. As a result, they are economical and environmentally friendly.
RESUMEN
In industries such as aerospace and automotive, drilling many holes is commonly required to assemble different structures where machined holes need to comply with tight geometric tolerances. Multi-spindle drilling using a poly-drill head is an industrial hole-making approach that allows drilling several holes simultaneously. Optimizing process parameters also improves machining processes. This work focuses on the optimization of drilling parameters and two drilling processes-namely, one-shot drilling and multi-hole drilling-using the Taguchi method. Analysis of variance and regression analysis was implemented to indicate the significance of drilling parameters and their impact on the measured responses i.e., surface roughness and hole size. From the Taguchi optimization, optimal drilling parameters were found to occur at a low cutting speed and feed rate using a poly-drill head. Furthermore, a fuzzy logic approach was employed to predict the surface roughness and hole size. It was found that the fuzzy measured values were in good agreement with the experimental values; therefore, the developed models can be effectively used to predict the surface roughness and hole size in multi-hole drilling. Moreover, confirmation tests were performed to validate that the Taguchi optimized levels and fuzzy developed models effectively represent the surface roughness and hole size.
