The surface roughness of hole machining greatly influences the mechanical properties of parts, such as early fatigue failure and corrosion resistance. The boring and trepanning association (BTA) deep hole drilling with axial vibration assistance is a compound machining process of the tool cutting and the guide block extrusion. At the same time, the surface of the hole wall is also ironed by the axial large amplitude and low-frequency vibration of the guide block. The surface-forming mechanism is very complicated, making it difficult to obtain an effective theoretical analytical model of the surface roughness of the hole wall through kinematic analysis. In order to achieve accurate prediction of the surface quality of the hole wall, the chip-breaking mechanism and the hole wall formation mode of BTA deep hole vibration drilling were analyzed. The influence of drilling spindle speed, feed, amplitude, and vibration frequency on the surface roughness of the hole wall during BTA deep hole vibration drilling was illustrated by a single-factor experiment. A four-factor and three-level test scheme was designed by using the Box–Behnken design (BBD) experimental design method. A surface roughness prediction model for hole wall machining was established based on the response surface methodology. The accuracy of the prediction model was analyzed through ANOVA, and the complex correlation coefficient of the model was 0.9948, indicating that the prediction model can better reflect the mapping relationship between vibration drilling parameters and surface roughness. After optimization analysis and experimental verification, the obtained vibration drilling parameters can achieve smaller surface roughness. The error between the predicted value of the model and the experimental measurement value is 8.65%. The established prediction model is reliable and can accurately predict the surface roughness of the hole wall of BTA deep hole axial vibration drilling, providing a theoretical basis for the surface quality control of the machining hole wall. It can be applied to process optimization in practical production.
