# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
## 5 Conclusions  
This study conducted side-milling experiments on SKD11 in two phases. The first phase aimed to explore the effects of different ultrasonic-assisted methods, including rotary, dual-axis and triple-axis, on milling performance. The better ultrasonic-assisted method obtained from the first phase was then adopted in the second phase both in conjunction with and without assistance from locally preheating with a laser, in which the Taguchi orthogonal array was applied for milling process parameter planning. The surface roughness and cutting-tool life served as the objective function and constraint, respectively, and the optimal combination of process parameters was thus determined by the characteristics of the smaller-the-better in the Taguchi method. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments  
Figure 21: Relationship between residuals and predicted value for various machine learning models and ensemble learning model.  
Figure 20: Relationship between residuals and predicted values for various machine learning models.  
were repeatedly performed to collect the data of cutting force and cutting-tool wear. An ensemble learning method was applied to construct a cutting-tool wear prediction model after the data underwent the processes of cleaning, feature extraction, data standardization, and feature selection. From the above analyses, the following conclusions can thus be drawn:  
1. As various ultrasonic-assisted methods compared with no assisted milling in the first phase experiments, it was found that ultrasonic-assisted machining could improve machining quality, reduce cutting force, and enhance surface roughness. Among them, rotary ultrasonic-assisted showed the best cutting performance.
2. As compared to the method without assisted milling, the rotary-assisted ultrasonic method could reduce surface roughness by approximately 5% and cutting force by 16%. When compared to rotary ultrasonic assistance, hybrid-assisted milling could reduce surface roughness by 30% and cutting force by 16%.
3. Among the multiple machine learning models in this study, extra trees exhibited the best performance, with an R\({}^{2}\) of 0.91737, RMSE of 0.30053, MSE of 0.09032, and MAE of 0.21134.
4. The ensemble voting regression model for cutting-tool wear prediction on the test dataset, can achieve an R\({}^{2}\) of 0.94576, RMSE of 0.24348, MSE of 0.05928, and MAE of 0.18182.
5. As compared to all the individual regression models in this study, the ensemble voting regression model exhibited the best generalization capabilities. This method can integrate the complementary predictive capabilities among different individual models and its generalization capabilities can be enhanced by combining the strengths of the multiple models. Therefore, ensemble machine learning is a feasible and effective method for monitoring cutting-tool wear.  
Conceptualization, supervision, writing, S.-Y.L.; methodology, analysis, C.-J.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical review and approval were waived for this study since it did not involve research with living organisms.  
Not applicable.  
Correspondence and requests for materials should be addressed to [PERSON]. The data are not publicly available due to patent in review.  
The authors acknowledge the National Formosa University for their support in this research.  
The authors declare no conflicts of interest.