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A tentative work from the perspective of cutting forces is carried out in this paper to analyse and assess the robotic belt grinding mechanisms. Firstly, a microscopic scale of cutting force model consisting of sliding, ploughing and cutting components is introduced, then the effects of force components on the machined surface roughness are explored based on the force control experiments, and finally a typical case on the robotic belt grinding of aero-engine blade with constant contact force is conducted to validate the practicality and effectiveness of force control. The results reveal two significant findings with respect to the high sliding force percentage and low cutting efficiency in comparison with the robotic belt grinding without force control. [ABSTRACT FROM AUTHOR]

• A novel material removal prediction model for robotic belt grinding is developed. • The material removal volume of single abrasive grain is shaped like a semi-elliptic cone. • The mean absolute percentage error of material removal rate prediction is less than 1.725. • The material removal depth is positively correlated with normal force and negatively correlated with belt linear speed and robot feed speed. • Optimization strategy is implemented to balance the grinding quality and efficiency. Comprehensive study of the microscopic material removal mechanism remains an open challenge facing the robotic belt grinding of complex geometries. In the present paper, a new material removal rate (MRR) model is developed underlying the motion trajectory of single spherical abrasive grain by taking into consideration the elastic deformation of the heterogeneous contact wheel. In this model, the contact pressure distribution at the contact wheel–workpiece interface is determined by converting the three-dimensional contact problem into a one-dimensional linear spring model with respect to the large deformation of the contact wheel. In addition, the combined modulus and Poisson's ratio of the heterogeneous contact wheel are further considered based on the geometric relation of the Young's modulus and Poisson's ratio. Compared with the existing MRR model, the proposed MRR model can significantly reduce both the root mean square error (RMSE) and mean absolute percentage error (MAPE) values from 2.401 to 1.725, and 18.426% to 14.942%, respectively. Particularly, an optimization strategy from the perspective of process parameters is implemented to balance the grinding quality and efficiency. Image, graphical abstract [ABSTRACT FROM AUTHOR]