The 7075-T7451 aluminum alloy, widely used in aerospace, aviation, and automotive fields for critical load-bearing components due to its excellent mechanical properties, suffers from residual stresses induced by thermo-mechanical coupling during milling, which deteriorate workpiece performance. This study explores how key milling parameters—spindle speed *n_c*, feed per tooth *f_z*, cutting depth *a_p*, and cutting width *a_e*—affect surface residual stress and cutting force via orthogonal experiments and finite element analysis (FEA). Results show *a_e* is critical for X-direction residual stresses, while *f_z* dominates Y-direction ones. Cutting force increases with *f_z*, *a_p*, and *a_e* but decreases with higher *n_c*. Multivariate regression-based prediction models for residual stress and cutting force were established, which effectively characterize parameter–response relationships with maximum prediction errors of 18.69% (residual stress) and 12.27% (cutting force), showing good engineering applicability. The findings provide theoretical and experimental foundations for multi-parameter optimization in aluminum alloy milling and residual stress/cutting force control, with satisfactory practical effectiveness.

Thin-walled partition frame parts are key load-bearing components in aerospace structures. Machining deformation directly affects assembly accuracy and service reliability. During milling, the release of residual stress caused by material removal is one of the main factors leading to deformation of thin-walled parts. To address this problem, aluminum alloy thin-walled partition frame parts are taken as the research object. A machining accuracy control method based on stress-sensitive region analysis is proposed. Key machining accuracy is used as the constraint condition. The concepts of stress-sensitive regions and sensitive directions are introduced. A coupled analysis model of residual stress and machining deformation is established. Residual stress is applied to element meshes in a finite element analysis platform and released under a free state. The influence of residual stress in different regions on part deformation is qualitatively identified. The dominant deformation directions are also determined. Based on these results, the milling tool path is specifically optimized. Strategies are adopted to avoid highly stress-sensitive regions or to control residual stress release by region. Overall machining deformation is effectively reduced. Experimental results show that the optimized tool path significantly suppresses part deformation compared with the conventional tool path. The flatness of the bottom surface is improved by up to 25.33%. The proposed method provides a feasible approach for machining process optimization of aerospace thin-walled parts with high precision.