## 6 Conclusions  
This study examines key quality characteristics during the machining process of mechanical products and predicts their processing quality. We propose a method for identifying these key machining quality characteristics and predicting their quality using information entropy and hyperparameter optimization of the XGBoost algorithm. The XGBoost model demonstrates effective prediction of the machining quality of marine diesel engine pistons while also identifying essential quality characteristics. The results and discussions indicate that optimizing both process parameters and model hyperparameters can significantly enhance the consistency of processing quality and the predictive accuracy of the model. This research provides a scientific basis for further optimizing the machining process of marine diesel engine pistons. The main content of this study is outlined as follows:  
1. Key machining quality characteristics are identified using information entropy, effectively addressing the shortcomings of other commonly used analytical methods that are heavily influenced by subjective factors from decision makers.  
2. In constructing the quality prediction model, this study integrates the XGBoost algorithm with Bayesian hyperparameter optimization for the first time, achieving a balance between prediction efficiency and accuracy of results.  
3. Using the machining process of a marine diesel engine piston as an example, the model is evaluated using the root mean square error (RMSE). The results indicate that the RMSE of the model on the training dataset is 0.012, while on the testing dataset, it is 0.0307, demonstrating high predictive accuracy. Experimental results further validate the excellent performance and strong generalization ability of the proposed prediction model.  
The research results present an effective quality prediction model specifically designed for multi-process machining. This model addresses the shortcomings of traditional quality prediction methods, such as their complex calculations and limited applicability. It is particularly beneficial for analyzing and predicting the machining of parts with intricate quality characteristics, which are often challenging for traditional models to handle. Moreover, this model not only facilitates the analysis and prediction of machining quality but also serves as a tool for optimizing machining processes in the future, thereby enhancing quality control in intelligent manufacturing. Furthermore, this method has shown significant potential to enhance machining quality while also reducing rework and scrap rates in the quality control of ship piston machining. This improvement is crucial for boosting the overall competitiveness of the manufacturing industry.  
Conceptualization, C.G. and D.Q.; methodology, D.Q.; software, C.G.; validation, Y.Z. ([PERSON]) and D.Q.; formal analysis, Y.Z. ([PERSON]), H.Z., Y.Z. ([PERSON]) and W.L.; investigation, C.G.; resources, D.Q.; data curation, C.G.; writing--original draft preparation, C.G.; writing--review and editing, D.Q., Y.Z. ([PERSON]), Y.Z. ([PERSON]), W.L. and H.Z.; visualization, C.G.; supervision, Y.Z. ([PERSON]); project administration, D.Q.; funding acquisition, D.Q. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Designing and Verifying Key Technologies of Marine Engines High Reliability in Major Special Project Ship Research Program of the Ministry of Industry and Information Technology (MIIT) of the People's Republic of China under Grant CB65N21-2-1.  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author, [PERSON], upon reasonable request.  
We have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.