ANN prediction and RSM optimization of cutting process parameters in boring operations using impact dampers

The cantilever shape of the boring bar induces chatter vibrations in boring operations. Chatter vibrations consequently lead to increase in tool wear. Present work focuses on the prediction and optimization of cutting process parameters using ANN and RSM methods for phosphor bronze damping material attached to the boring tool. All-geared head lathe with temperature measurement setup was used to conduct experiments for various levels of cutting speed, depth of cut and position of damper from the cutting edge. Tool wear was measured using profilometer, while the temperature and tool wear were accurately predicted using the developed ANN model. The minimum value of temperature of 2800 C and tool wear of 0.13 mm were obtained by using Response Surface Methodology for the following input conditions: cutting speed of 300 rpm, depth of cut of 0.25 mm and damper position of 65 mm from the cutting edge.