Press-braking bending is usually performed after face milling to obtain the specified curvature of variety mechanical parts, which is widely applied in the aircraft manufacturing. The surface layer residual stress on workpiece caused by milling has an important influence on subsequent forming. The influences of surface residual stress caused by milling operation on press-braking bending has been investigated analytically and experimentally in this study. The analytical model for springback prediction based on plane strain assumption is proposed using the material model of Mises yield criterion and Swift exponential hardening law, and takes into account of the effects of milling-induced residual stress. Three levels of residual stress are introduced in the test plates through milling operation with different cutting speed. The analytical results are compared with the bending experiments of 7050-T7451 Al-alloy plates. Results show that the accuracy of the contour obtained by the proposed model is improved when surface residual stress is considered. Furthermore, the influence of surface residual stress on springback is discussed. It is revealed that surface tensile residual stress increases springback and surface compressive residual stress contributes positively to the final shape of bent specimens.

The assembly interface topography of precision machinery exhibits complex multi-scale geometric features, including roughness, waviness, and form error, which critically influence assembly accuracy and tolerance analysis. To address the lack of adaptivity in existing separation criteria, this paper proposes a multi-scale digital modeling approach oriented toward tolerance analysis of precision assembly interfaces, based on a fractal-wavelet framework. Firstly, multiple Weierstrass–Mandelbrot functions with independent fractal dimensions are superposed to construct a multi-fractal topography model with controllable multi-scale characteristics, grounded in the power spectral density energy additivity property. Subsequently, wavelet functions are employed to hierarchically decompose the topography height field information. The effects of the compact support length and vanishing moments of the wavelet functions on the decomposition performance are analyzed to establish a clear basis for their selection. Finally, an adaptive multi-scale separation criterion based on wavelet energy K-means clustering is then proposed, with the optimal number of scale classes determined by maximizing the silhouette coefficient, eliminating reliance on empirical thresholds. Case study results show that the fused waviness-and-form-error model retains 94.8% of the original energy while reducing convex peak count by over 90%, significantly simplifying the interface microstructure for downstream tolerance computation. The proposed method provides a high-fidelity, adaptive digital foundation for assembly accuracy prediction of precision interfaces.