In the fabrication process of thin-walled micro-parts, both micro-cutting tools and thin-walled micro-parts have the characteristics of small size and low stiffness. Therefore, the regenerative chatter during the machining process cannot be ignored. The influence of the tool runout error and actual trochoidal trajectories of the cutting edge on micro-milling forces should also be considered comprehensively. In this paper, the tool runout error in the micro-milling process is first analysed, and an instantaneous undeformed chip thickness model is established considering the runout error. On this basis, the dynamic deformation of the micro-cutting tool and thin-walled micro-part is studied, and an instantaneous, undeformed, chip-thickness model is proposed with the consideration of both the runout error and dynamic deformation. The dynamic parameters of the machining system are obtained using the receptance coupling method. Finally, thin-walled micro-part machining experiments are carried out, and the obtained results of micro-milling force simulation based on the proposed model are compared with the experimental results. The results indicate that the micro-milling force modelling, by taking the influence of machining dynamics into account, has better prediction accuracy, and the difference between the predicted resultant forces and the experimental results is less than 11%.

Micro-milling has found extensive applications in machining components with thin-walled microstructures, such as terahertz slow-wave structures, microfluidic chips, and micro-molds. Due to the influence of size effects, micro-milling exhibits higher specific energy consumption compared with traditional milling, implying that more energy is consumed to remove a unit volume of material, particularly in challenging-to-machine materials like Ti-6Al-4V. Historically, research on parameter optimization for micro-milling has predominantly focused on enhancing machining quality and efficiency, with limited attention given to energy efficiency. However, in the context of the “double carbon” strategy, energy conservation and emissions reduction have garnered significant attention in the manufacturing industry. Therefore, this paper proposes a micro-milling parameter-based power consumption model. Based on this, a specific energy consumption model can be obtained. Moreover, evolutionary algorithms are utilized for the optimization of micro-milling parameters, which aims to achieve comprehensive enhancements in both machinability and sustainability. The optimization objectives encompass improving surface quality, dimensional accuracy, material removal rate, and specific energy consumption during the micro-milling process for thin-walled micro-structures. Among them, NSGA-III achieves the best optimization results. Under conditions in which cutting energy consumption and processing efficiency are very close, the optimization outcomes based on NSGA-III lead to the best machining quality, including the minimum surface roughness and dimensional errors, and the largest surface fractal dimension. The optimal combination of micro-milling parameters is n = 28,800 rpm, f_z = 2.6 μm/t, and a_p = 62 μm.

Micro-milling technology is an efficient method for machining titanium alloy thin-walled micro parts. However, the machinability of thin-walled microstructures can be greatly affected by chatters due to their weak stiffness, especially in difficult-to-machine materials like titanium alloys. In order to obtain high-quality thin-walled micro parts, a chatter mitigation strategy is presented and its mechanism is analyzed in this paper. The titanium alloy workpiece is submerged into a kind of high-viscosity fluid, which can improve machining stability by increasing the damping of the micro-milling process. The stability lobe diagrams (SLD) are established, and the machinability is analyzed and compared under different machining conditions. The cutting force, surface roughness, micro-tool wear, and dimension accuracy of thin-walled micro parts are selected as the evaluation criteria for the machinability assessment. Well-designed micro-milling experiments are carried out under dry, lubrication, and chatter mitigation conditions using the same cutting process parameters. The results show that the use of cutting fluid can effectively improve machinability under stable cutting processes, but the effect of cutting fluid is weakened under chatter machining. The proposed chatter mitigation strategy can suppress regenerative chatter throughout the micro-milling process for thin-walled micro parts and improve their machinability comprehensively.

The surface integrity and machining accuracy of thin-walled micro parts are significantly affected by micro-milling parameters mostly because of their weak stiffness. Furthermore, there is still a lack of studies focusing on parameters optimization for the fabrication of thin-walled microscale parts. In this paper, an innovative approach is proposed for the optimization of machining parameters with the objectives of surface quality and dimension accuracy, which integrates the Taguchi method, principal component analysis method (PCA) and the Non-dominated sorting genetic algorithm (NSGA-II). In the study, surface arithmetic average height S_a, surface root mean square height S_q, and 3-D fractal dimension D_s are selected to evaluate surface quality. Then micro-milling experiments are conducted based on the Taguchi method. According to the experimental results, the significance of machining parameters can be determined by range analysis. Besides, regression models for the responses are developed comparatively, and the PCA method is employed for dimension reduction of the optimization objective space. Finally, two combinations of machining parameters with the highest satisfaction are obtained through NSGA-II, and verification experiments are carried out. The results show that the surface quality and dimension accuracy of the thin-walled microscale parts can be simultaneously improved by using the proposed approach.