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 f_t 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., f_t = 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.

本文建立了一种预测外围铣削加工表面形貌和各向异性织构方向的新模型。与以前的模型不同，此模型考虑了最大数量的影响因素，即刀具设置误差（径向偏移和轴向倾斜），静态刀具偏斜，强制振动，颤振以及随机刀具磨削误差和磨损（STGEW）。STGEW，通过正态分布（定量μ，σ ²），被结合到模型中的第一次。在稳定和不稳定的铣削条件下，对3D表面形貌，各向异性纹理方向，2D表面轮廓或粗糙度的模型预测与实验或已发布的结果非常吻合。

已经发现，静态挠度会稍微减弱刀具设置误差对粗糙度的影响。即使使用新鲜的刀具进行加工，也可以使用切削长度非常短（<15 mm）且磨损（VB〜5μm）的刀具，固有的STGEW会在铣削表面上产生平行于进给方向的不同几何形状的随机刮擦。STGEW扩大了轴向粗糙度的差。当标准偏差σ与进给速率f _t的比率较小时，STGEW会在进给方向上引起额外的表面纹理，该纹理与进给标记所贡献的轴向纹理并存。作为σ增加：（1）刮痕逐渐消除了进给痕迹，并且表面纹理仅沿进给方向定向；（2）在两个方向上，粗糙度的均值和方差增加，并且在高σ时，轴向粗糙度优先于横向韧性。研究发现，即使具有较小的σ（例如σ = [0.01μm，0.1μm]），STGEW也是影响表面形貌，各向异性表面纹理方向和粗糙度的重要因素，尤其是在较低的情况下进给加工，例如f _t= 0.05 mm，通常用于难切削材料的精铣。该模型可用于可靠，准确地预测外围铣削操作中的表面形貌，表面粗糙度和表面纹理方向。