Mechanical surface treatments are needed to perform on components for fatigue life enhancement by introducing beneficial compressive residual stress and material strengthening. In this study, the combined turning with low plasticity burnishing (LPB) surface modification process was performed for the sake of improving mechanical properties of Inconel 718. Firstly, the evolution of microstructure and residual stress after the LPB process were analyzed with the aid of electron backscatter diffraction (EBSD) and X-ray diffraction (XRD), respectively. Secondly, the tensile behavior of treated samples was investigated through tension tests. Finally, the micro-strengthening mechanism of Inconel 718, induced by the LPB process, was revealed. The results show that the peak compressive stress is increased by a factor of 4.2 after the LPB process. The grain refinement induced by the LPB process is attributed to the increase of average misorientation and the formation of high angle grain boundaries (HAGBs). The enhanced yield strength depends on the decreased average spacing and the increased HAGBs.

The micro-machining scale effect makes it challenging to forecast and control the process parameters of the micro-milling process, which makes the micro-flanking-milling of weak-rigidity micro-thin-walled parts prone to deformation. To determine the critical cutting parameters for chip formation in the micro-milling of curved thin-walled parts at the mesoscale, the strain-softening effect of titanium alloy during high-speed milling and the scale effect of mesoscale cutting were comprehensively considered and a finite element prediction model for curved micro-thin-wall micro-milling was established to determine the critical milling parameters for effective material removal. Based on the determined milling parameters, an experimental design of response surface optimization was carried out. Based on the response surface methodology, a data-driven quantitative model with milling process parameters as design variables and deformation amounts as response variables was established to reveal the influence mechanism of multiple milling process parameters on machining accuracy. Based on the process requirements for deformation control in the micro-milling of curved thin-walled structures, dynamic optimization of the milling process parameters was performed using an improved NSGA-III algorithm to obtain non-dominated solutions. A visual ranking and a determination of the unique solution were conducted using the entropy weight–TOPSIS method. Finally, micro-milling validation experiments were carried out using the optimal parameter combination. The optimal solution for the process parameters of the arc-shaped micro-thin-wall micro-milling of titanium alloy established by the institute provides a relevant reference and guidance for mesoscale arc-shaped thin-wall micro-milling.