Modelling of grinding force and material removal rate (MRR) has been widely investigated for wheel grinding which often has a preset cutting depth, but is rather lacking for sand belt grinding. For robotic belt grinding where the normal force often remains constant, the cutting depth of individual grain varies as the abrasive grains wear with grinding time increasing. It is, therefore, a challenge to accurately predict the tangential force and resulted MRR, and subsequently control the finish profile. This paper develops grinding force model and material removal rate model based on single grain force for robotic belt grinding. It divides the whole grinding process into three stages: initial stage, steady stage and accelerated stage, based on the degree of grain wear, analyses the grinding force of rubbing, ploughing and cutting effects and MRR at each stage. By studying the distribution of grains and penetration depth of each grain, the grinding force and MRR are calculated. Experimental work on stainless steel 304 shows that the maximum errors of the tangential force prediction is 10.9% and that of MRR is 14.4%. The proposed models not only reveal the grinding mechanism but also predict the grinding force and MRR.

砂轮磨削的力和材料去除率（MRR）的模型已被广泛研究，砂轮磨削通常具有预设的切削深度，而砂带砂轮却缺乏。对于法向力通常保持恒定的机械手砂带磨削，随着磨削时间的增加，单个磨粒的切削深度会随着磨粒的磨损而变化。因此，准确预测切向力和最终的MRR并随后控制精加工轮廓是一项挑战。本文开发了基于单颗粒力的机器人皮带磨削磨削力模型和材料去除率模型。它将整个磨削过程分为三个阶段：初始阶段，稳定阶段和加速阶段，根据磨粒的磨损程度，分析摩擦的磨削力，耕作和切割效果以及每个阶段的MRR。通过研究晶粒的分布和每个晶粒的渗透深度，可以计算出磨削力和MRR。不锈钢304的实验工作表明，切向力预测的最大误差为10.9％，MRR的最大误差为14.4％。提出的模型不仅揭示了磨削机理，而且还预测了磨削力和MRR。