Abstract: In the process of orthodontic treatment, the relative sliding trend between the archwire and bracket produces friction force, which reduces the effective force and affects the performance and efficiency of the treatment. The current orthodontic friction force prediction method fails to comprehensively consider the geometrical relationship, mechanical relationship and physical parameters between the archwire and the bracket, which is difficult to provide accurate and reliable prediction for doctors. This paper aims to provide a high-precision quantitative prediction method, and investigate the main factors affecting the orthodontic friction and its changing principle. In view of the mechanical factors affecting orthodontic friction, the orthodontic friction is divided into three contact components according to the relative contact between the archwire and the bracket. A modeling method of orthodontic friction force prediction taking into account the contact angle was proposed based on principle of component force superposition. Taking the three adjacent brackets as an example, the geometrical relationship, mechanical relationship and physical parameters between the archwire and the bracket were analyzed. Firstly, the contact angle was calculated. Secondly, the constraint force was modeled based on the beam deformation theory, the classical friction was modeled based on the first friction theory, and the notching resistance was modeled based on the mechanical relationship. Finally, the orthodontic friction force in the two contact cases was obtained, which were bilateral contact and unilateral contact between the archwire and the bracket groove. In the experiment, the finite sliding method was used to measure the orthodontic friction force, and an orthodontic simulation dentition with three brackets was designed. A six-dimensional force sensor-based orthodontic friction force measurement system was built to measure friction force at a constant ligature pressure, constant sliding speed and within a limited sliding stroke of 3 mm. The friction prediction models for the two contact cases were validated by friction measurements with different archwire-bracket combinations and four sets of contact angles (0°, 3°, 6°, and 9°), respectively. The deviation rate between the experimental data and the theoretical data of the prediction model was in the range of 0.55%~9.65%. In the case of bilateral contact, orthodontic friction was negatively correlated with the width of bracket groove, and positively correlated with the cross-sectional size of archwire. The width of bracket groove affected the friction to a greater extent than the cross-sectional size of archwire. In addition, orthodontic friction was more sensitive to changes in cross-sectional size for round archwire and to changes in bracket groove width for rectangular archwire. With constant archwire-bracket parameters, orthodontic friction was positively correlated with contact angle, and as contact angle increased, friction increased more rapidly with the stainless steel round archwire than with the stainless steel rectangular archwire. When combined with the same bracket, the friction generated by a round archwire with small cross-sectional area reached or even exceeded that of a rectangular archwire with a larger cross-sectional area under the condition of bracket restraint. The friction between the domestic stainless steel round archwire and the bracket was higher than the friction between the Australian round archwire and the bracket under the same conditions. The prediction model can provide a theoretical basis for the physician to clarify the relationship between orthodontic appliance parameters-friction force-orthodontic force. In the future, the model can be used to establish an orthodontic friction prediction system to accurately predict individualized orthodontic tribological behavior by means of theoretical calculations and simulations, thus aiding digital orthodontic treatment and achieving light orthodontic treatment. Biological factors will be further taken into account in the prediction model to simulate the real environment in the mouth as much as possible.