## 1 Introduction  
Titanium alloy materials are widely used in the manufacturing of aerospace parts [1], which are gradually becoming larger in volume and more complex. The tools that are generally applied to materials are difficult to machine, thereby resulting in increased tool consumption and burden. Currently, matching the tool performance with processing requirements is very important. If the function or structural design cannot meet the present high specialization requirements, a customized performance design is necessary for the development of milling tools. The realization of a customized performance design is the primary premise in intelligent design; however, there are currently few related studies focused on this. As the most front-end equipment in the manufacturing process, cutting tools are regarded as the tooth of a machine. Thus, the quality of parts is dependent on tool performance, which gradually decreases with tool wear until the surface quality does not meet the processing requirements. Tool wear is a key bridge that connects tool status to the surface quality. Surface quality is one of the most influential processing requirements, as well as an important indicator, for judging tool performance. Wear of the milling tool during the machining process directly affects surface quality [2]. Surface quality refers to the microscopic unevenness of the machined surface, also known as roughness. The working performance, reliability, and life of the workpiece all depend, to a large extent, on the surface quality of the finished good. To achieve the designed performance, the relationship between the wear and surface quality should be accurately established first. In terms of the relation establishment, [PERSON] and [PERSON] [3; 4; 5] proposed and validated a tool shape-performance-application design method. This integrated the constraints relation between geometry, force, heat, and materials, as well as its theories, into design features. By combining with algorithms of programmed tool design platforms, the tool customization design was achieved. However, this is a constraint relationship rather than a certain functional relationship and does not intuitively reflect the relationship between the parameters and performance. In addition, [PERSON] [6] used the NURBS curve and the scan surface to generate the geometry of milling tools. By controlling the polygon and scanning the trajectory to control the tool shape, a precise geometric model was established for a class of milling tools. [PERSON], [PERSON], and [PERSON] [7; 8] successfully constructed a wear rate model with turning tool design parameters by equivalent geometric and physical volumes. [PERSON] [9] established a relationship between the tool geometry and the milling force to simulate the circumferential wear. Furthermore, [PERSON] and [PERSON], et al. [10] created a tool wear predictive mathematical model for milling parts of complicated geometry. Their model used factors on the entry impact duration of the cutting edge as well as a commercial CAM system. The parameters of the developed model were determined based on the experimental results. The abovementioned studies employed different methods to establish a certain functional relationship between the design parameters and the wear or cutting force. However, the wear and milling forces are not a direct performance index or processing requirement in manufacturing. Thus, the relation between the performance and design parameters requires further improvement. To achieve performance design, a certain functional relationship between the performance and design parameters must be established. In summary, based on the shape and application conditions of the tool, a functional relationship between the wear and design parameters has been constructed [11]. The wear can also greatly impact surface qualities such as the roughness. At present, research on the surface roughness mainly relies on modeling methods, algorithms, experimental methods, and combinations thereof.