Modelling of machined surface topography and anisotropic texture direction considering stochastic tool grinding error and wear in peripheral milling

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 (<15 mm) and wear (VB∼5 μm), inherent STGEW creates random scratches with different geometries parallel to feed direction on the milled surface. STGEW expands the difference of roughness in axial direction. STGEW induces an additional surface texture in feed direction, which co-exist with axial texture contributed by feed marks when standard deviation σ to feed rate ft ratio is small. As σ increases, (1) feed marks are gradually obliterated by scratches, and surface texture is only oriented in feed direction; (2) the mean and variance of roughness in both directions increase, and axial roughness prevails transverse toughness at high σ. The study discovers that, STGEW, even with small σ (e.g., σ=[0.01 μm, 0.1 μm]), is an important factor affecting surface topography, anisotropic surface texture direction and roughness that cannot be ignored, especially in the case of low-feed machining, e.g., ft = 0.05 mm, which is commonly adopted in finish-milling of difficult-to-cut materials. The proposed model can be used to reliably and accurately predict surface topography, surface roughness and surface texture direction in peripheral milling operation.