Simulation study on multi-process milling deformation of frame thin-walled parts considering initial residual stress

Due to the advantages of high strength, low density and good cutting performance of aluminum alloy materials, aluminum alloy thin-walled parts have been widely used in aerospace structural parts. However, due to the thin wall thickness and low stiffness of thin-walled parts, machining deformation has become a significant problem in processing aerospace structural parts. The evolution of residual stress and cutting load in the workpiece during machining are the main causes of machining deformation of thin-walled parts. To accurately predict the machining deformation of thin-walled parts, this paper proposes a new modeling method that can characterize the influence of initial residual stress and machine cutting load on the machining deformation of the workpiece. The life and death element method is used to simulate the material removal and machining deformation in the rough machining process, and the tool-work contact simulation method is used to simulate the material removal and machining deformation in the finishing process. Experiments show that compared with the traditional simulation method, the new modeling method considering the initial residual stress and cutting load improves the simulation accuracy by 11.2% and can accurately predict the machining deformation of thin-walled parts. Finally, the deformation of the workpiece under different finishing allowances is analyzed using this method. The results show that with the increase in finishing allowance, the milling force in the milling process, the surface residual stress, and the subsurface residual stress after milling will increase, and then the local deformation of the workpiece will gradually increase. The research results can be used to accurately predict the machining deformation of the workpiece under different process parameters guide the selection of finishing allowance during the processing of thin-walled parts, and provide a basis for further optimization of process parameters.