Ultrasonic vibration-assisted grinding is an effective method for improving the surface quality of brittle and hard materials. The grinding force is one of the key factors affecting the surface quality of the machined surface, and this has been investigated both experimentally and theoretically. However, the influence of process parameters on grinding forces during longitudinal ultrasonic vibration-assisted grinding (LUVAG) of alumina ceramics has not been studied in depth. To investigate the effects of various parameters on the grinding forces of LUVAG of alumina ceramics, the kinematic theory of LUVAG was combined with the effective cutting trajectory of transient single-grain abrasives and the effective removal of transient material to develop a LUVAG alumina ceramic grinding force model and a LUVAG endface grinding force model. The experimental results showed that the grinding depth (a(p)) and table speed (v(w)) were positively correlated with the tangential grinding force (F-t) and the normal grinding force (F-n) (when v(w) increased from 400 to 1000 mm/min, F-t increased by about 10 to 15% and F-n increased by 30 to 45%; when a(p) increased from 5 mu m to 20 mu m, F-t increased by 20 to 30%, F-n increases by 25 to 30%). The ultrasonic amplitude (A) is negatively correlated with the grinding force (when A increases from 4 to 12 mu m, F-t decreases by 5 to 10% and F-n decreases by 25 to 30%). The grinding wheel speed (v(s)) was positively correlated with F-t but negatively correlated with F-n (when the grinding wheel speed increased from 1400 to 3500r/min, F-t increased by 20 to 36% and F-n decreased by 30 to 46%). According to the range analysis, F-t is the minimum when a(p)=5 mu m, v(s) =1400 r/min, A=12 mu m, v(w)=600 mm/min, and F-n is the minimum when a(p) =5 mu m, v(s) =3500 r/min, v(w) =600 mm/min, A=12 mu m. The results of the theoretical model calculations show that the predicted values of the grinding forces for the given parameters are in general agreement with the experimental results. The maximum relative mean error between the theoretical and experimental results was 12.3%, while the overall relative mean error was 4.94%. At the same time, it was demonstrated that the model can assist orthogonal experiments for factor selection and scheme optimization, which has important practical application value.