A predictive wear model in grinding using single-layer electroplated cBN tools

Official URL: https://dx.doi.org/10.1016/j.jmapro.2024.07.121

Wheel wear is a complex phenomenon significantly affecting grinding performance. While microscopic grit observation can detect various wear mechanisms, a predictive model is required to anticipate the total wear behavior under diverse grinding conditions. This study incorporates all wear mechanisms, including pullout, attrition, and fracture, into an analytical model aimed at determining improved grinding conditions. The grinding particles are approximated as standard constitutive geometries to model the wheel surface topography and the grit height reduction obtained from the wheel surface roughness is correlated with wear. The present model introduces an innovative approach to tracking and predicting wheel wear performance, eliminating the need for time-consuming grit micro-observation. Numerous grinding tests employing different tools were conducted, revealing strong agreement between the experimental results and the model predictions. The Application of this approach for predicting prolonged tool life is also demonstrated. Finally, the paper discusses how these findings contribute to a deeper understanding of the fundamental wear mechanisms in the grinding process.