Multi-Objective Optimization of Micro-Milling Parameters-The Trade-Offs between Machining Quality, Efficiency, and Sustainability in the Fabrication of Thin-Walled Microstructures

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 micromilling parameters is n = 28,800 rpm, fz = 2.6 mu m/t, and ap = 62 mu m.