# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
## 1 Introduction
[PERSON] and [PERSON] [PERSON] and [PERSON] (2018) conducted high-speed ball nose end milling experiments on AISI A2 tool steel using PCBN and coated tungsten carbide tools on a 5-axis machining center, with variations in milling length and cutting speed to assess changes in the tool wear mechanism and surface roughness. The results revealed that coated carbide tools performed poorly within the high-speed machining range, while high-CBN-content PCBN tools are relatively suitable for this hardened steel. Under normal cutting speeds, the surface roughness of coated carbide tools was twice that of the PCBN tools. In addition, it led to work hardening on the workpiece, increased hardness, material drag, surface cracks, and microstructural changes. The failure mode of PCBN tools is flaking and edge chipping due to the fact that toughness is insufficient for application when the cutting edge is worn out. Moreover, low-CBN-content PCBN tools lack of the required toughness and abrasion resistance for the application. [PERSON] et al. [PERSON] et al. (2018) conducted high-speed face milling of the hardened steel SKD11 to investigate the failure patterns and failure mechanism of a double-layer-coated cemented carbide tool. Additionally, the effects of cutting-tool damage on cutting forces, surface roughness, and tool failure surface morphology were also studied. The results showed that the dominating failure pattern of the coated tool in this study was fatigue fracture, accompanied by chipping. At low cutting speeds, many fatigue cracks were observed on the rake face, while fatigue striations and river patterns were observed at the fatigue crack propagation zone with the increase in cutting speed. In addition, fatigue steps and secondary cracks were found on the fatigue propagation area under higher cutting speeds. The main fracture modes were intergranular and transgranular fractures at lower and higher cutting speeds, respectively. The cutting force had a large influence on tool life in the initial tool wear stage. However, when the tool breakage and coating peeling occurred, the cutting temperature was the dominating factor for tool life. With increased flank wear and cutting forces, the values of machined surface roughness constantly increased. With regard to the literature review on machine learning, [PERSON] et al. [PERSON] et al. (2018) applied two different forms of machine learning classification trees to conduct a study, utilizing vibration acceleration measurements as the physical parameters, to predict the possibility of tool wear during the milling process of EN-GJL-250 cast iron. The experiments were carried out using a four-edge cemented-carbide end mill cutter, with vibration acceleration serving as the input data for the models to forecast tool wear. The study obtained the vibration acceleration results during the experiments and utilized machine learning techniques, particularly decision trees, to classify tool wear conditions. The experimental results showed that, compared to regression models, machine learning methods exhibited significantly smaller errors in predicting tool wear than those reached by the regression model. [PERSON] et al. [PERSON] et al. (2018) used different machine tools conducting two milling-tool life tests to collect cutting performance data, such as cutting force, vibration, and cutting sound to construct a tool-wear monitoring model of titanium alloy milling. In the experiments, the above-mentioned raw signals were captured by multiple sensors and features were extracted on time, frequency, and time-frequency domains. Then, feature dimensionality reduction was achieved using an information measurement-based feature selection method, introducing symmetrical uncertainty to select relevant features. Finally, a multiclass support vector machine model was developed to identify the wear stages of the tool.