# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
## 1 Introduction  
When the demand for new products rapidly emerges, bringing with it new mold requirements, manufacturers must efficiently design and produce molds while maintaining precision. Among the various mold requirements, SKD11 alloy tool steel is a widely used material for mold manufacturing. SKD11 finds extensive applications in the automotive, household appliance, and electronics manufacturing industries. It is a high-carbon and high-chromium alloy steel that exhibits exceptionally high hardness and toughness. This steel also incorporates elements such as molybdenum (Mo) and vanadium (V), givingit wear resistance, low deformation, high fatigue strength, and impact resistance characteristics. These qualities make it one of the preferred materials for various applications, including stamping dies and plastic molds. Heat-treated SKD11 alloy tool steel can achieve a hardness of HRC 58-62. However, these excellent properties of SKD11 make it a difficult-to-cut material. Difficult-to-cut materials have poor machinability, high-cutting-power requirements, and tend to generate a significant amount of cutting heat, resulting in elevated cutting temperatures, reduced tool life, and decreased production efficiency. As cutting-tool wear increases, the magnitude and fluctuation pattern of cutting forces also undergo variations. Cutting-tool wear can lead to irregular fluctuations in cutting forces, causing vibrations during the cutting processes. By analyzing the characteristics of cutting force fluctuations, the degree of the cutting-tool wear can be inferred, allowing for the prediction of remaining cutting-tool life. Therefore, in the process of establishing a cutting-tool wear prediction model, precise monitoring and analysis of cutting force fluctuations contribute to achieving real-time awareness of the cutting-tool condition, enhancing the stability and efficiency of the manufacturing processes. The necessity of establishing a cutting-tool wear prediction model lies in enhancing the efficiency, quality, and safety of the manufacturing processes. By predicting cutting-tool wear, the production line can promptly initiate cutting-tool replacement, thereby reducing downtime and production costs. Furthermore, the cutting-tool wear prediction model can be employed to optimize cutting parameters, improving machining efficiency. This helps prevent excessive wear or premature cutting-tool replacement, thus saving costs and extending cutting-tool life. With the advancement of the times and the rapid development of technology, product iteration speeds have increased. At the same time, there is a need to deal with higher manufacturing complexity and shorter production cycles, which, undoubtedly, raise research and development costs, as well as production pressures in the industry. Finding more efficient and cost-effective ways to shorten production cycles is a challenge for the manufacturing sector [1; 2]. The high strength, hardness, and toughness of SKD11 result in poor machinability. To meet the precision, stability, and shorter production cycles required for molds, there are numerous factors to consider during the machining process. These factors include, but are not limited to, machining parameters, machining assistance, and the impact of tool wear. Firstly, machining parameters such as cutting speed and feed rate play a crucial role. Cutting speed directly affects the machining time and tool wear rate, while the feed rate impacts material removal rates and surface roughness. Secondly, machining assistance significantly influence the machining process. Effective machining assistance can extend tool life and enhance product quality. Lastly, tool wear directly affects machining efficiency and workpiece quality. As tool wear increases, it results in reduced accuracy and increased surface roughness. Excessive tool wear can also lead to increased cutting forces, accelerating further tool wear and reducing production efficiency.