An intelligent system approach for surface roughness and vibrations prediction in cylindrical grinding

This work aims to develop an adaptive network-based fuzzy inference system (ANFIS) for surface roughness and vibration prediction in cylindrical grinding. The system uses a piezoelectric accelerometer to generate a signal related to grinding features and surface roughness. To accomplish such a goal, an experimental study was carried out and consisted of 27 runs in a cylindrical grinding machine operating with an aluminium oxide grinding wheel and AISI 8620 steel workpiece. The workpiece speed, feed rate and depth of cut were used as an input to ANFIS, which in turn outputs surface roughness (Ra) and vibration (a(z)). Different neuro-fuzzy parameters were adopted during the training process of the system in order to improve online monitoring and prediction. Experimental validation runs were conducted to compare the measured surface roughness values with the values predicted online. The comparison shows that the gauss-shaped membership function achieved an online prediction accuracy of 99%.