Aeronautical thin-walled frame workpieces are usually obtained by milling aluminum alloy plates. The residual stress within the workpiece has a significant influence on the deformation due to the relatively low rigidity of the workpiece. To accurately predict the milling-induced residual stress, this paper describes an orthogonal experiment for milling 7075 aluminum alloy plates. The milling-induced residual stress at different surface depths of the workpiece, without initial stress, is obtained. The influence of the milling parameters on the residual stress is revealed. The parameters include milling speed, feed per tooth, milling width, and cutting depth. The experimental results show that the residual stress depth in the workpiece surface is within 0.12 mm, and the residual stress depth of the end milling is slightly greater than that of the side milling. The calculation models of residual stress and milling parameters for two milling methods are formulated based on regression analysis, and the sensitivity coefficients of parameters to residual stress are calculated. The residual stress prediction model for milling 7075 aluminum alloy plates is proposed based on a back-propagation neural network and genetic algorithm. The findings suggest that the proposed model has a high accuracy, and the prediction error is between 0–14 MPa. It provides basic data for machining deformation prediction of aluminum alloy thin-walled workpieces, which has significant application potential.

Linear friction welding (LFW) is a kind of advanced manufacturing technology and used mainly in the manufacturing of aircraft engine bladed disks (blisks) currently. However, the residual stress evolution of TC17 titanium alloy during LFW is complex and its distribution is difficult to characterize. In this study, the residual stress of welding was studied using numerical simulation and experimental methods. The results showed that the maximum temperature on the welded surface was up to 1000 °C and the cooling rates in the lengthwise, widthwise, and normal direction with the same distance from the center of the weld were 456 °C/s, 448 °C/s, and 232 °C/s, respectively. The lengthwise stress on the welding surface was the largest, followed by the widthwise stress and normal stress. Among the three factors affecting welding stress, the upsetting force played a leading role, followed by the vibration amplitude and frequency of the welded parts. By optimizing the process parameters: upsetting force 18.2 kN, vibration amplitude 2.5 mm, vibration frequency 40 Hz, a 30% decrease of the maximum residual stress could be achieved compared to that without optimization. The residual stress before and after welding parameter optimization was measured by the contour method, and the measured results were in good agreement with the simulation results, which verified the effectiveness of parameter optimization on residual stress controlling.