Peripheral milling, as a highly flexible machining method, is extensively employed at various stages of manufacturing. Small-diameter end mills excel in this process, adeptly managing complex geometrical features, which makes them particularly well-suited for advanced applications in aerospace, precision mold making, and medical device manufacturing. However, due to the tool’s small diameter and weak rigidity, this method is highly susceptible to cutting forces and vibrations, leading to unstable machining quality and challenges in parameter optimization. Existing surface roughness prediction theories either completely overlook the impact of vibrations, rely on measuring vibration signals to modify surface roughness models, or conduct the classical finite element analysis to predict deformation, which is prone to problems of mesh distortion and numerical instability under aggressive cutting conditions. Therefore, this paper proposes an improved Z-value map (Z-MAP) surface morphology simulation model for peripheral milling with Small-diameter end mills, taking into account the effects of cutting vibrations and cutting forces during the process. Initially, the prediction of cutting forces under various cutting parameters is achieved based on Smoothed Particle Hydrodynamics (SPH) and a micro-element cutting force model. Subsequently, a kinematic model of the cutting process is constructed to predict cutting vibrations. These vibrations are then integrated into the Z-MAP model to enable surface morphology prediction. Experiments with various cutting parameters are performed to benchmark the proposed methodology and validate the pronounced role of tool vibration in surface morphology formation. Lastly, the research analyzes the impact of spindle speed, feed rate, and tool overhang on surface morphology and roughness. Results indicate that the milled surface roughness increases with feed rate and overhang length while decreasing with higher spindle speeds. By combining SPH, the micro-element cutting model, and Z-MAP, this approach provides efficient, accurate predictions of peripheral milling morphology, offering practical guidance for optimizing machining parameters.
Liu et al. (Sat,) studied this question.