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Optimisation of grinding processes involves enhancing the surface quality and reducing the cost of manufacturing through reduction of power consumptions. Recent research works have indicated the minimum quantity lubrication (MQL) system is used to achieve near dry machining of alloys and hard materials. This study aims to provide an experimental analysis of the grinding process during machining of aluminium alloy (Al6061-T6). MQL nanofluid was used as the lubricant for the grinding operations. The lubricant was formed by suspending silicon dioxide nanoparticles in canola vegetable oil. The effect of input parameters (i.e. nanoparticle concentration, depth of cut, air pressure and feed rate) on the grinding forces and surface quality was studied. Adaptive neuro-fuzzy inference system (ANFIS) prediction modelling was used to predict the specific normal force, specific tangential force and surface quality, the ANFIS models were found to have prediction accuracies of 97.4, 96.6 and 98.5 per cent, respectively. Further study shows that both the specific grinding forces and surface roughness are inversely proportional to the nanofluid concentration. Also, the depth of cut and table feed rate were found to have a directly proportional relationship with both the grinding forces and surface roughness. Moreover, higher MQL air pressure was found to offer better delivery of the atomised nanofluid into the grinding region.

Grinding experiments were performed using MQL nanofluid as the lubricant. The lubricant was formed by suspending silicon dioxide nanoparticles in canola vegetable oil. The effect of input parameters (i.e. nanoparticle concentration, depth of cut, air pressure and feed rate) on the grinding forces and surface quality has been studied.

The grinding process parameters were optimised using Taguchi S/N ratio analysis, whereas the prediction of the response parameters was done using ANFIS modelling technique. The developed ANFIS models for predicting the specific normal force, specific tangential force and surface quality were found to have prediction accuracies of 97.4, 96.6 and 98.5 per cent, respectively. Further findings show that both the specific grinding forces and surface roughness are inversely proportional to the percentage of nanoparticle concentration in the lubricant. Also, the depth of cut and table feed rate were found to exhibit a direct proportional relationship with both the grinding forces and surface roughness, while high MQL air pressure was observed to offer more efficient delivery of the atomised nanofluid into the grinding region.

The work can applied into manufacturing industries to prevent unnecessary trials and material wastages.

The purpose of this study is to develop an artificial intelligent model for predicting the outcomes of MQL grinding of the aluminium alloy material using ANFIS modelling technique.