Sharp abrasion of the abrasive belt always causes surface quality to deteriorate in the robotic grinding of thin-walled blades, which has stymied the further advancement of robot machining technology. A novel belt wear prediction approach based on the acoustic emissions (AE) signal is presented in this study to monitor belt wear conditions to achieve the expected machining result. A time-varying model of belt wear height and tangential force is initially established by employing the force analysis for various belt wear states. Next, a relationship between AE signal power and belt wear height is established utilizing the energy principle. Finally, robotic machining experiments on Ti-6Al-4V alloy workpieces are performed to achieve accurate belt wear prediction of an average error of approximately 10%, and the influence analysis of belt wear on the surface quality is further investigated to extend effective service life of abrasive belt by about 20% and realize the desired blade’s machining quality of surface roughness Ra$<$0.4 $\mathrm{\mu }\text{m}$ and contour accuracy $\le$ $\pm 0.17\mathrm{mm}$.

在薄壁叶片的机器人磨削中，砂带的剧烈磨损往往会导致表面质量变差，阻碍了机器人加工技术的进一步进步。本研究提出了一种基于声发射 (AE) 信号的新型皮带磨损预测方法，以监测皮带磨损状况以实现预期的加工结果。通过对各种皮带磨损状态进行力分析，初步建立了皮带磨损高度和切向力的时变模型。接下来，利用能量原理建立AE信号功率与皮带磨损高度之间的关系。最后，对 Ti-6Al-4V 合金工件进行了机器人加工实验，实现了平均误差约为 10% 的皮带磨损准确预测，$<$0.4$\mathrm{\mu }\text{米}$和轮廓精度$\le$ $\pm 0.17\mathrm{毫米}$.