An experimental study on the prediction of grinding wheel dressing intervals by relating wheel loading and surface roughness

Grinding being the most commonly performed finishing process and requires frequent dressing operation to restore the original cutting capability of the abrasive wheel. The present work focuses on predicting the dressing intervals based on the final surface finish. The surface finish was primarily affected by the wheel parameters, grinding parameters and wheel loading. Wheel parameters were kept constant in this research work and grinding parameters were optimised using ANN-PSO approach. Experiments were conducted on cylindrical grinding machine with AISI D2 steel as the work specimen. Wheel loading is quantitatively evaluated by machine vision and image processing technique. Artificial neural network was used for developing the computational model for correlating the wheel loading and surface roughness data. This developed predictive model was used for determining the dressing intervals based on the surface finish requirement for different applications.