## 1 Introduction  
The optimization of process parameters is regarded as a crucial means to improve machining performance, the results of which are dependent on the optimization objectives and methods. In prior research, optimization objectives have mainly focused on technical requirements and production efficiency, while energy consumption and environmental degradation have often been neglected [1]. Sustainable manufacturing technology presents new challenges for researchers in the context of carbon peaking and carbon neutrality. As some of the main energy-consuming pieces of equipment in the manufacturing industry, the primary form of energy consumption for machine tools is electricity. There are approximately 8 million units of machine tools in China, indicating significant potential for energy saving. Therefore, balancing machining quality and efficiency while reducing the energy consumption of machine tools has gradually become a prominent research focus in the manufacturing industry [2]. [PERSON] et al. employed the simulated annealing algorithm to optimize the spindle speed and feed rate in the single-point turning process, focusing on machining energy consumption while considering cutting force, cutting power, and surface roughness as constraints. The optimized process parameters resulted in a 19.28% reduction in energy consumption [3]. [PERSON] et al. conducted simultaneous optimization of toolpaths and process parameters during the drilling process using NSGA-II, with energy consumption, machining time, and surface roughness as the optimization objectives. The experimental results demonstrated that this approach reduced the energy consumption and machining time by 57.7% and 66.4%, respectively, while significantly improving the surface quality [4]. In order to achieve a balance between milling sustainability, production efficiency, and machining quality, [PERSON] et al. employed the response surface methodology in combination with the grey relational analysis method to optimize the spindle speed, feed rate, cutting depth, and cutting width in the milling process. The optimized process parameters were found to enhance both the material removal rate and surface quality while reducing energy consumption by 18.1% [5]. [PERSON] et al. conducted an optimization study on micro-milling process parameters, including spindle speed, feed per tooth, and cutting depth, with the objective of minimizing power consumption, extending tool life, improving surface roughness, and enhancing tool holder strength. The cuckoo search algorithm and grey wolf optimization algorithm were used to enhance global search capabilities. As a result, a 7.89% reduction in energy consumption was achieved [6]. [PERSON] et al. conducted optimizations of the energy consumption and machining time in milling processes. The results indicated that the optimization outcomes based on the improved ant colony algorithm surpassed those obtained with NSGA-II, yielding higher productivity at the same energy consumption level [7]. Rational parameter selection can lead to comprehensive improvements in machinability and sustainability at lower costs. The current research has mostly focused on conventional machine tools, with limited studies on micro-milling. The established energy consumption model is the basis for analyzing energy characteristics, improving energy efficiency, and minimizing energy consumption in machining. Currently, CNC machine tool energy consumption models can be generally categorized into three types: material-removal-rate-based models, specific process-parameter-based models, and cutting-force-based models [8]. The power consumption model based on material removal rates is simple in form and has clear physical significance. However, research has shown that different combinations of process parameters can yield different energy consumption values at the same material removal rate [9; 10; 11]. The variation in process parameters is a significant factor influencing the energy consumption of machine tools [13].