## 5 Conclusions  
Aiming at the problems of drilling damage and tool wear when drilling GFRP, experiments on the characteristics of the drilling damage and tool wear of GFRP were carried out in this paper. It was found that the thrust force, vibration amplitude, the number of processed holes, feed rate and cutting speed were the main factors affecting the drilling damage and tool wear. By comprehensive consideration of the above processing dynamic characteristics and processing parameters, an improved CCM Grey Wolf algorithm for a BP neural network was developed to predict the tool wear and delamination factors. The main conclusions were as follows:  
1. The higher the cutting speed, the smaller the thrust force, and the lower the entrance and exit delamination factors. With the increasing feed rate, the thrust force increased gradually, and the delamination factor of the entrance and exit also increased. The delamination factor was directly related to the feed rate and cutting speed, and these two parameters can be selected as input layers for delamination factor prediction.  
Figure 17: The correlation coefficient between predicted and experimental values. **(a)** R-value of the training set for VB. **(b)** R-value of the test set for VB. **(c)** R-value of the training set and test set for VB. **(d)** R-value of the training set for \(F_{d}R\). **(e)** R-value of the test set for \(F_{d}R\). **(f)** R-value of the training set and test set for \(F_{d}R\). **(g)** R-value of the training set for \(F_{d}C\). **(h)** R-value of the test set for \(F_{d}C\). **(i)** R-value of the training set and test set for \(F_{d}C\).  
2. Due to continuous friction with the glass fiber and resin matrix in the machining area, the flank face wear was significant, and the expansion of the tool wear zone increased the contact area with the material, which further exacerbated the wear. The VB of the flank face can be selected as the performance index to evaluate the wear.
3. The higher the feed rate and cutting speed, the more severe the tool wear. The feed rate, cutting speed and the number of holes were the main factors influencing the tool wear and should be used as the input data of the prediction model. Because the worn tool cannot cut the material sufficiently, fibers were increasingly torn at the edge of the entrance and exit. Therefore, the value of the delamination factors at the entrance and exit reflected the change of the value of VB.
4. A BP neural network model improved by the CCM Grey Wolf algorithm was proposed to predict the processing quality and tool wear of GFRP drilling. The maximum prediction error of the improved BP neural network model was reduced by 71.2%, and the RMS prediction error was reduced by 63.82%. The prediction error of \(F_{d}R\) was not more than 3%, and the prediction error of \(F_{d}C\) was not more than 1%. The prediction results showed that the neural network model optimized by the improved Grey Wolf algorithm can better predict the drilling quality and tool wear of GFRP, and has higher accuracy, optimization efficiency and better robustness than the ordinary BP neural network.  
Conceptualization, S.H., Y.F. and C.C.; methodology, S.H. and Y.M.; software, Y.F. and Y.M.; validation, S.H., H.L. and G.Z.; formal analysis, H.L. and X.H.; investigation, Y.M, Y.F. and X.H.; resources, S.H. and C.C.; data curation, S.H., Y.F., Y.M. and X.H.; writing--original draft preparation, S.H. and H.L.; writing--review and editing, S.H.; visualization, Y.F., and C.C.; supervision, S.H. and G.Z.; project administration, S.H. and G.Z.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Nature Science Foundation of China (No. 51965004).  
Not applicable.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.