###### Abstract
The findings highlighted the significance of feed rate and cutting conditions, leading to a 30% reduction in tool wear, a 22% improvement in surface roughness, and a 4% decrease in specific cutting energy under the proposed optimal parameters [8]. In recent years, various empirical studies have explored metal micro-machining. Predominant techniques include EDM machining, laser micro-machining, ultrasonic micro-machining, and mechanical micro-machining. Mechanical machining, particularly micro-milling, has gained significant attention from researchers due to its flexibility and productivity in micro-level machining. From 2000 to 2020, research in micro-milling has primarily focused on size effects, cutting forces, vibrations, tool wear, burr formation, surface roughness, and process optimization. [PERSON] et al. [9] conducted a study on process parameter optimization in turning aluminum alloy 7075. Using the Taguchi method with an L27 array, they investigated how cutting speed, feed rate, and depth of cut influenced material removal rate (MRR), surface roughness, and cutting force. Their findings revealed that a depth of cut of 2 mm, a cutting speed of 1600 rpm, and a feed rate of 0.25 mm/min resulted in optimal conditions for achieving high MRR while reducing cutting force and surface roughness. [PERSON] et al. [10] conducted an experimental study on surface roughness during slot end milling of AL2014-T6. Their research aimed to investigate how surface roughness was affected by the cutting environment and tool geometry when milling slots in AL2014-T6. Input parameters included cutting-edge concavity, feed rate, depth of cut, and axial relief angles of the end mill. Surface roughness models were developed using experimental data and the response surface methodology (RSM) under both dry cutting and coolant conditions. [PERSON] et al. [11] investigated the impact of machining parameters on force and surface quality during micro end milling of brass. They identified feed/tooth as a significant factor in micro-machining performance. A study was conducted on milling processes to investigate the impact of machining parameters on surface roughness, burr width, and energy consumption. Cutting speed significantly influenced specific cutting energy and burr width during down-milling, with contributions of 55% and 47.98%, respectively. Conversely, the number of inserts had a notable impact on surface roughness and burr width during up-milling, contributing 68.74% and 35%, respectively. Confirmation tests validated the findings [12]. Researchers conducted a study on machining titanium alloy Ti-6 Al-4V using cryogenic cooling. They developed an energy consumption map, finding that cutting speed had a greater impact on energy consumption. This cryogenic energy map reduced energy consumption by up to 16% and increased productivity by up to 156% by optimizing machining parameters, thereby promoting sustainability and cleaner production [13]. [PERSON] et al. [14] investigated the impact of cutting parameters on surface quality and burr width in micro-milling of Ti-6 Al-4V alloy. Spindle speed, feed per tooth, and depth of cut were controlled factors. Taguchi-based gray relational analysis optimized these parameters for minimal burr width and surface roughness (Ra). ANOVA determined the contribution percentages of these factors. The results showed that a feed rate of 0.25 \(\upmu\)m/tooth achieved the best surface quality, whereas an ap value of 0.1 mm and an n value of 10,000 rev/min minimized surface roughness (Ra). [PERSON] et al. [15] conducted an empirical study on burr formation during vibration-assisted micro-milling of titanium grade 5 (Ti-6 Al-4V). Their findings indicated that burr size could be effectively reduced by optimizing the vibration parameters during the micro-milling process. [PERSON] et al. [16] conducted a study on hybrid cooling-assisted turning of titanium grade 9 (Ti-4 Al-2.5V).