Investigation of chatter stability in boring tool and tool wear prediction using neural network

Chatter vibrations were induced during boring process due to cantilever shape of boring bars. These vibrations further leads to an increase in temperature of the boring tool which increases the tool wear. This present investigation is focused on selection of suitable damping material for the boring tool in various positions in order to reduce tool wear. Various damping materials such as copper, cast iron, brass, phosphor bronze, gun metal, steel and aluminium are considered for analysis. A conventional lathe which is attached with a temperature measurement setup was used to conduct experiments for various levels of speed, depth of cut, damping materials and its position from the cutting edge to measure tool wear and temperature. Tool wear and temperature were accurately predicted using artificial neural network model and it was compared with the experimental values.