## 1 Introduction
However, due to the numerous features involved in machining, comprehensively predicting each quality feature incurs high computational costs. To address this, the present study focuses on analyzing key machining quality features based on information entropy and predicting these features to achieve a balance between efficiency and accuracy. Common machining methods for parts include turning, milling, drilling, and grinding. For example, [PERSON] et al. [11] successfully predicted tool performance during turning by conducting experiments on C45 material using cutting tools with various materials and coatings, employing the Taylor model and least squares method. [PERSON] et al. [12] evaluated eight machine learning models to assess their performance in predicting the cutting force for AISI 52100 bearing steel hard turning through five-fold cross-validation. Their findings revealed that Gaussian process regression (GPR) and decision tree regression models performed best, with GPR also providing prediction uncertainty, thereby aiding in optimizing cutting parameters and reducing tool wear. Additionally, [PERSON] and [PERSON] [13] utilized the ANFIS-QPSO method to successfully predict and optimize the surface roughness of AISI 304 stainless steel during turning, showcasing the method's high accuracy and robustness. Nonetheless, each processing quality prediction method has its own strengths and limitations, making it essential to select the most suitable method to ensure the accuracy and applicability of the prediction results. Common quality prediction methods include time series prediction [14], regression analysis, the Taylor model [11], and machine learning techniques [15]. Time series prediction, while valuable, encounters practical challenges such as difficulties in addressing nonlinear relationships, high data requirements, and the need for long, complete historical datasets, which are often difficult to obtain [16]. Regression analysis is straightforward and easy to implement, but when dealing with a large number of explanatory variables, the model can easily suffer from overfitting or underfitting issues [17]. The Taylor model is frequently used to predict tool life and cutting force, yet it struggles to cope with complex or dynamic machining environments due to its limited scope, lack of dynamic response, and reliance on experimental data. On the other hand, machine learning, a branch of artificial intelligence, excels at handling complex tasks by uncovering hidden patterns and associations within datasets [18]. Popular machine learning methods for quality prediction include decision trees [19], random forests [20], support vector machines (SVMs) [21], and XGBoost [22]. For example, [PERSON] et al. [23] applied the decision tree algorithm to analyze resistance spot welding (RSW) data from an automobile manufacturer, constructing a regression tree and extracting decision rules to predict nugget width, which highlighted the influence of design and process parameters. Similarly, [PERSON] et al. [24] employed the weighted random forest (WRF) algorithm to develop a slab quality prediction model based on multiple process parameters, effectively addressing sample imbalance in the continuous casting process and validating the method's effectiveness through real-time data. Furthermore, [PERSON] et al. [25] proposed a method to obtain contour error of tool rotation via a workpiece shape-tool contour mapping identification test. They optimized the least squares support vector machine (LS-SVM) model using a genetic algorithm (GA), demonstrating that the GA-LS-SVM model outperformed the unoptimized LS-SVM model in error prediction. Among these methods, XGBoost stands out for its ability to handle complex nonlinear relationships, automatically select features, and perform efficiently and accurately with large-scale datasets [26].