Abstract:
Grinding is a finish machining task in which excess material is removed from the surface of the workpiece material with grinding wheel containing abrasive grains. High temperature is generated in the work-tool interface because of rubbing and friction. This elevated temperature has various detrimental effects on the performance and the longevity of the material being ground. So an effective solution to this universal problem has been a matter of great concern for the researchers. Convention flood cooling method has several drawbacks like health problems to the workers, environment pollution, soil contamination and huge wastage of expensive material. As a result, some alternative cooling techniques comes to the spot like high pressure cooling, Minimum Quantity Lubrication, cryogenic cooling, compressed air etc. The techniques are better in terms of reducing grinding force, surface roughness, residual stress, wheel loading and surface burning. Application of Minimum Quantity Lubrication is economically viable and environment friendly.
This study focuses on the effects carbon nanofluid on grindability of hardened steel. Mixing carbon nanotube with the cutting fluid has demonstrated excellent cooling and lubricating properties. Adding compressed air at the time of machining operation enhances surface properties by keeping the abrasive grains sharp. Main purpose of this thesis is analyze the effect of nanofluid and compressed air and their flow rate for grinding AISI 1060 steel at some industrially available two different speeds, four different infeed and another four different cutting conditions. Graphical representation of experimental investigation reveals that CNT mixed MQL with compressed air has the best cooling and lubricating properties among the four different cutting conditions used in this research work. Four independent variables (wheel speed, infeed, material hardness and cutting condition) are used in formulating the mathematical model using Response Surface Methodology (RSM). Statistical analysis suggests that there are strong correlations between these parameters with the output response. The model is validated by comparing with the experimental data and found accurate reasonably. Slight variation in the result was due to natural processes and some physical phenomena.
