# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
## 1 Introduction
The results demonstrated that the classifier using multiple sensors achieved an overall identification accuracy of 96.7%, while the classifiers based on single-force sensors and vibration sensors exhibited performances of 96.7% and 92.5%, respectively. [PERSON] et al. [18] conducted a study on the prediction of tool wear in various machining processes using machine learning. They performed different machining experiments such as milling, drilling, and turning for IN718 material, and each machining process employed six tools in different states of wear. Initially, experimental data on cutting force from three machining processes were utilized to extract 15 features. Subsequently, singular spectrum analysis and principal component analysis were employed for feature selection and dimensionality reduction, respectively. The severity of tool wear was categorized into five levels, and a prediction was performed using LightGBM and an ensemble model combining six algorithms: LR, RF, CART, NB, SVM, and KNN. The results demonstrated the outstanding performance of the LightGBM model in terms of speed and efficiency, effectively handling huge datasets. Additionally, the model overcame some of the deficiencies present in previous tool wear models, such as high dimensionality and overfitting. This improvement is attributed to the application of dropout and early stopping techniques. [PERSON] et al. [19] proposed a milling tool wear prediction method for multi-condition classification and rapid selection of prediction models based on linear discriminant analysis (LDA) and the ensemble method. First, the whole wear process of the cutting tool is divided into five stages. Next, the time domain, frequency domain, and wavelet packet of the raw data are extracted, and the RF and XGBoost algorithms are used for multi-algorithm feature screening. The LDA algorithm is used to fuse and downscale the screened features, which are then combined with the K-means clustering algorithm to group similar working conditions. The samples are expanded and balanced using the SMOTE algorithm. Then, separate regression and classification prediction models are created for each group of working conditions. These models are integrated with an algorithm to determine the optimal prediction model for each group of working conditions. Compared with the traditional transfer learning algorithms, the proposed method allows for the rapid selection of a prediction model, which is achieved by dividing new conditions into groups with reduced dimensionality. The test results demonstrate the method's effectiveness in addressing the challenges of selecting prediction models and reducing the number of model transfers. [PERSON] et al. [20] explored the use of data mining techniques for monitoring the tool condition in metal cutting. The study focused on the analysis of vibration signals, particularly employing the pseudo-local singular spectrum analysis (SSA) method. The methodology includes the integration of SSA and band-pass filtering to process vibration signals, aiming to eliminate irrelevant noise components. A major advantage of this approach is its ability to extract relevant information from high-frequency vibration components. Additionally, the study explores the use of the SSA method to define features that are highly sensitive to tool wear, enabling the monitoring of tool condition. The results demonstrate that this method exhibits a high recognition rate in identifying tool wear conditions. However, its recognition rate is lower for dysfunctions in the cutting process, such as chip jamming and unproductive passes. This highlights the potential application of data mining techniques in tool condition monitoring. [PERSON] et al. [21] proposed an indirect tool-wear measurement method based on multi-information fusion by hybrid machine-learning techniques, to increase the utilization efficiency of multi-sensor signals and improve the measurement accuracy of tool wear.