## 1 Introduction
Since the 1970s, various effective techniques, including weighted methods, genetic algorithms, particle swarm optimization, and fuzzy logic control, have been developed to address the multiple conflicting objectives inherent in complex systems effectively [15; 16; 17]. [PERSON] et al. [18] tackled the multi-objective optimization issue resulting from the combined influence of milling parameters and tool geometric parameters through integrating least squares regression modeling and finite element simulation; he successfully accomplished effective control over various milling objectives with the VAEA optimization algorithm. [PERSON] et al. [19] highlighted a conflict between reducing milling force and lowering tip temperature during cutting tool processing. When cutting speeds are comparable, enhancing cutting efficiency can be accomplished by adjusting both depth of cut and feed rate. [PERSON] [20] made use of the particle swarm optimization algorithm to optimize the surface roughness, cutting temperature, and cutting energy consumption in the course of the hard milling process of AISI H13 steel. The results suggest that the cutting energy consumption can be lowered by 14% following optimization. [PERSON] [21] utilized response surface methodology to assess the impacts of milling parameters on machining characteristics. Relying on genetic algorithms, he attained the most favorable combination of milling parameters that reduced production time and unit production cost. [PERSON] [22] put forward a TOPSIS model for multi-criteria decision making, which integrates Taguchi methodology, merging surface roughness and tool wear into a single response after determining the optimal processing parameters. This approach demonstrates that employing variable cutting speeds during machining considerably enhances both tool life and surface roughness in contrast to traditional constant-speed methods. Multi-objective optimization techniques provide significant benefits in engineering applications; however, many recent studies primarily depend on theoretical models and simulation analyses, lacking sufficient backing from large-scale experimental data, which limits the applicability of certain optimization algorithms in practical production environments. Moreover, differences in milling characteristics due to varying materials and process parameters add further complexity to the optimization procedure. Genetic algorithms serve as a powerful optimization method that can effectively tackle complex issues with multiple peaks by simulating biological evolution processes while being less prone to local optima, thus, enabling efficient solutions for challenges faced in real-world engineering scenarios [23; 24]. This research seeks to forecast the trend of variation in the wear width on the rake face and milling force by developing a model for tool wear and a multi-quadratic regression model. Orthogonal experiments are utilized to select key factors (including spindle speed, feed rate, and axial milling depth), thus, allowing for a thorough evaluation of each factor's impact on the results while differentiating between primary and secondary influences, which aids in optimizing experimental design and improving overall efficiency. Additionally, a genetic algorithm is employed for the multi-objective optimization of material removal rate, the wear of the rake face, and milling force to determine optimal strategies that offer a scientific basis for real-world production practices.