A digital twin-based framework for selection of grinding conditions towards improved productivity and part quality

Determining grinding conditions to achieve part quality and production rate requirements is a challenging task. Due to the complexity of the process and many affecting factors, grinding conditions are chosen conservatively, mostly based on experience or handbooks to eliminate quality problems. Thus, an integrated modeling system is required to select grinding conditions in a systematic approach for high-performance grinding. The key feature required of such a system is the capability of producing results in a wide range of grinding conditions and parameters without the necessity of conducting extensive experimentation. This is feasible only by adopting geometrical-physical-based modeling for grinding which is a challenging task since most of the grinding process research is based on experimental methods involving calibration tests. In this study, by considering a grit representation of the grinding wheel and grit-workpiece interaction coupled with the material deformation model, a multi-dimensional modeling system capable of process predictions for a wide range of grinding parameters and conditions has been developed. Using this system, a digital twin-based framework is established to select grinding conditions in an efficient and proactive manner. Based on the simulation results of this new integrated system, some general guidelines are recommended with a systematic approach. This approach is demonstrated in a case study considering the process constraints showing how the material removal rate (MRR) can be maximized without sacrificing the surface integrity which is the main concern in this process. The proposed methodology offers a new outlook on grinding parameter selection, to be used in an integrated digital twin to increase part quality and productivity while respecting the constraints.