Performance improvement of minimum quantity lubrication (MQL) technique in surface grinding by modeling and optimization

Abstract

Abstract Although cutting fluids have many technological advantages in grinding operation, they have serious ecological and economic problems. On the other hand, in dry grinding, there exist challenges of probable thermal damages on the workpiece surface. One efficient alternative is minimum quantity lubrication (MQL) technique. MQL technique uses a spray of small oil droplets in a compressed air jet. The lubricant is sprayed directly into the cutting zone. Therefore, it provides efficient lubrication and improves cutting performance without using huge flow of fluids. In this research, the effect of mechanical properties of workpiece (especially hardness) on performance of MQL in grinding is investigated. Two soft steels (CK45 and S305) and two hard steels (HSS and 100Cr6) were ground with an aluminum oxide grinding wheel as case studies. A comparative study of three cooling conditions including: dry, conventional fluid cooling and MQL have been carried out. Output parameters were grinding forces, friction coefficient, surface roughness, surface morphology and form of the chips. The results show that MQL can considerably decreases grinding forces and friction coefficient in soft and hard steels. Furthermore, the surface finish and quality are significantly better when MQL technique is applied in grinding of hard steels. But in case of soft steels, surface finish is the worst in comparison to other grinding environments. So, it is anticipated that MQL may not be suitable for soft steels limiting its usability range towards harder steels. Hence, to improve the performance of grinding of soft steels using MQL technique, modeling and optimization of surface roughness have been conducted. The second order surface roughness estimation model based on RSM (response surface method) has been developed. Three process parameters including depth of cut, cutting speed and feed rate have been considered for the model development. The model has been tested and validated through ANOVA (analysis of variance). Finally, the GA (genetic algorithm) gives minimum value of surface roughness and the corresponding optimal machining parameters was in close agreement with experimental test.