Face milling commonly generates surface quality of roughness or variation, especially severe for the milling of large-scale components with complex surface geometry such as cylinder block, engine head, and valve body. Thus surface variation serves as an important indicator both for machining parameter selection and components’ service performance. Conversely the optimization of machining process is a vital objective to improve the surface quality and its service life of machined components. Many researchers have dedicated to the prediction of machined surface variation generated by face milling using numerical or experimental methods. However, the numerical methods based on finite element analysis (FEA) are good at predicting local deformation of workpiece under instantaneous milling force, particularly applied for online compensation in face milling. Whereas experimental methods can only be used to estimate whole surface variation through reverse correlation analysis of measured data and processing variables. Therefore, an efficient and comprehensive numerical model is highly desired for the prediction of surface variation of entire surface. This study proposes a coupled numerical simulation method, updating FE model literarily based on the integration of data from ABAQUS and MATLAB, to predict surface variation induced by the face milling of large-scale components with complex surfaces. Using the coupled model, the 3D variation of large-scale surface can be successfully simulated by considering face milling process including dynamic milling force, spiral curve of milling trajectory, and intermittently rotating contact characteristics. Surface variation is finally represented with point cloud from totally iterative FE analysis and verified by face milling experiment. Result shows that the new prediction method can simulate surface variation of complex components. Based on the verified model, a set of numerical analyses are conducted to evaluate the effects of local stiffness non-homogenization and milling force variation on machined surface variation. It demonstrates that surface variation with surface peaks and concaves is strongly correlated with local stiffness non-homogenization especially in feed direction. Thus the coupled prediction method provides a theoretical and efficient way to study surface variation induced by face milling of large-scale complex components.

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