Abstract
The Inconel 718 alloy, a difficult-to-cut superalloy with an extensive demand on aircraft and nuclear industries, being a low thermally conductive material exhibits a poor machinability. Consequently, the cutting tool is severely affected, and the tool cost is increased. In this context, an intelligent solution is presented in this paper—investigation of minimum quantity lubrication (MQL) and the selection of best machining conditions using evolutionary optimization techniques. A series of milling experiments on Inconel 718 alloy was conducted under dry, conventional flood, and MQL cooling modes. Afterward, the particle swarm optimization (PSO) and bacteria foraging optimization (BFO) were employed to optimize the cutting speed, feed rate, and depth-of-cut to minimize the flank wear (VBmax) parameter of a cutting tool. Though both the PSO and BFO models performed well, the validated results showed the superiority of PSO. Furthermore, it was found that the MQL performed better than the dry and flood cooling condition with respect to the reduction of the tool flank wear.
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