LAUSR

Abstract This paper establishes a new model for predicting machined surface topography and anisotropic texture direction in peripheral milling. Unlike previous models, this model takes into account the largest number of influential factors, i.e., tool setting error (radial offset and axial tilt), static tool deflection, forced vibration, chatter vibration, and stochastic tool grinding error and wear (STGEW). STGEW, quantified by the normal distribution (μ, σ2), is incorporated into the model for the first time. The model predictions for 3D surface topography, anisotropic texture direction, 2D surface profile or roughness agree remarkably well with experimental or published results, under both stable and unstable milling conditions. It is found that static deflection slightly attenuates the effect of tool setting error on roughness. Even machining with fresh tools or tools with very small cutting length (