Abstract:
In modern material science, engineers are constantly attracting and striving to develop nanohybrid Aluminum based Metal Matrix Composite (AMMC) materials due to their outstanding tribological, microstructural and mechanical qualities like lightweight, ductile, highly conductive, superior malleability, high strength and high specific modulus. Moreover, the demand for Aluminum based Metal Matrix Composite is increasing day by day because of their massive applications in various automobile, military, aviation, aerospace, structural, transportation, marine and other manufacturing industries due to their high stiffness, high strength-to-volume portion, deterioration resistance, and exceptional wear resistance. Nano particles like CNTs, Silicon Carbide and Alumina have created a great impact to produce advanced engineering composites. The mechanical and thermal property upgrades accomplished by expansion of CNT in Aluminum metal lattice frameworks. The addition of Carbon Nanotubes potentially helps in further improving the tensile strength of the metal matrix composite. So, Metal matrix composite with nano tubing provide enhanced mechanical features compared to traditional reinforcement.
In this research work, mechanical properties and machinability of carbon nanotube reinforced aluminum metal matrix composite has been compared with traditional aluminum metal matrix. Moreover, turning operation of carbon nanotube reinforced aluminum metal matrix composite was performed under both dry and MQL cooling condition. Cutting speed, feed rate and depth of cut have been considered as input cutting parameters whereas resultant outputs are cutting temperature, surface roughness, cutting force and tool wear. It is found that application of MQL resulted in maximum 16.62%, 31.28%, and 27.58% lesser cutting temperature, surface roughness, and cutting force by than machining without any fluid. Using response surface methodology, optimum cutting condition has been found while machining fabricated composite under MQL condition, the optimum cutting parameters which yielded the desired surface roughness Ra = 1.03µm, is follows: 1 mm of t, 168 m/min of Vc and 0.103 mm/rev of feed rate. Finally, A predictive model of surface roughness was developed using artificial neural network (ANN) which has been validated against the experimentally found results. For ANN developed model, regression value is found to be 0.98 for carbon nanotube reinforced aluminum metal matrix composite under MQL condition which is very close to 1, thus justifying the efficacy of the developed model.
