A bearing is an important component in contemporary machinery and equipment, whose main function is to support the mechanical rotator, reduce the friction coefficient during its movement, and guarantee the turning accuracy. However, assembly of a nanoscale bearing and sleeve is a challenging process for micro-nano mechanical manufacturers. Hence, we show the cold-welding mechanism of a copper nanobearing-nanosleeve via molecular dynamic simulations. We demonstrate that it is feasible to assemble a bearing and sleeve at the nanoscale to form a stable mechanism. The effect of temperature in the range of 150 to 750 K is investigated. As the temperature rises, the mechanical strength and the weld stress of the welded structures markedly decrease, accompanied by the observation of increasing disorder magnitude. This comparison study is believed to facilitate future mechanical processing and structural nano-assembly of metallic elements for better mechanical performance.

In high-speed machining, linear toolpaths constructed from a series of short line segments are widely used but inevitably introduce tangent and curvature discontinuities at segment junctions, which may cause feedrate fluctuation and contouring error. To address this problem, this study proposes a real-time corner smoothing and feedrate interpolation method based on dual cubic Bézier transition curves and an optimal error assignment model. The main contribution lies in coupling analytical corner rounding with error allocation: the approximation error and maximum curvature of the transition curves are obtained explicitly, while the allowable tolerance is optimally distributed between approximation error and chord error so that the overall trajectory error remains within the prescribed bound. A jerk-limited look-ahead interpolator is then developed through reverse scanning and forward interpolation to satisfy geometric constraints, drive constraints, and feedrate commands. Simulation results for a three-dimensional toolpath show that the approximation error, chord error, and total trajectory error are all constrained within the preset tolerance of 0.05 mm. In the mask-machining case, the proposed method reduces the machining time to 13.9 s, corresponding to reductions of approximately 70% and 25% compared with the method without look-ahead and the method with look-ahead only, respectively. These results indicate that the proposed framework can improve motion smoothness and machining efficiency while maintaining trajectory accuracy.