Drilling is a process of making hole in a solid body with the help of multipoint cutting tool. Here to improve the life of tool, in order to minimize the production cost, to increase
surface roughness different types of coating are applying over it, that coating soft/hard/ soft+ hard. Tool selection depends upon nature of drilling; dry, with coolant. This paper gives review of different coating techniques and its effectiveness by measuring deviation in hole diameter, surface roughness, and wear measurement. A back propagation neural network is preferred instead of radial basis neural network for the prediction of tool wear. It is considered that tool wear depends on cutting speed, feed, thrust force and torque.
This paper presents an investigation on the optimisation and the effect of cutting parameters on multiple performance characteristics (work piece surface roughness, spindle load) obtained by turning operations. A CNMG 09 03 08-PF carbide insert as tool and the HCHC steel as work piece material were used in experiments. The work piece material was machined under different settings of feed rate, depth of cut, cutting speed on a CNC lathe model-TL1 (HAAS). The results showed that cutting speed and feed rate were the dominant variables on multiple cutting performance characteristics. An optimum parameter combination was obtained by using experimental analysis.
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