## 6 Conclusions  
The wear of the abrasive belt has a great impact on the processing efficiency and processing accuracy, so it is necessary to accurately judge the wear degree of the abrasive belt. However, at present, due to the limitation of the processing environment, it is difficult to achieve real-time detection, so the equipment can only be suspended for manual detection, wasting time, cost, and labor cost. Therefore, this paper proposes a method for predicting abrasive belt wear based on back-propagation (BP) neural network, which greatly simplifies the detection process and reduce the cost. First, the experiment was based on ultra-depth-of-field detection technology, different parameter combinations are used to measure the degree of abrasive belt wear, and the effects of different grinding speeds, different contact pressures and different work piece materials on the abrasive belt wear rate are obtained. Then by utilizing the artificial intelligence BP neural network method, the degree of abrasive belt wears was predicted. Finally, the surface quality inspection technology was employed to evaluate the surface quality of work pieces processed by abrasive belts with different degrees of wear.  
Based on the proposed method, experiments in this paper are conducted with different experimental parameter combinations. Total of 18 sets of data are selected for BP neural network training and to establish the combination of various process parameters, such as the non-linear mapping relationship between grinding speed, contact pressure, belt type, etc., and wear rate, which are verified by 9 sets of data. By utilizing this artificial intelligence method, the wear of the abrasive belt can be predicted more quickly and accurately. Therefore, the manufacturing processing guided through this method greatly improves the processing efficiency.  
Figure 20: Comparison of the true value of the test sample and the predicted value based on other method.  
In the future, further research will be carried out from the following two aspects:  
On the one hand, in the sand belt wear measurement experiment, due to the non-uniformity and randomness of sand belt planting process and the instability of measuring equipment, there exist the errors in the experimental measurement data. In the future research, effective methods will be studied to reduce the impact of experimental errors on the prediction results.  
On the other hand, when using artificial intelligence BP neural network for predictive analysis, the fixed learning rate is used for training, and the idea of gradient descent is used to find the best weight to reduce the network error. The learning rate of the network determines the weight change in the learning process. If the learning rate is too high, oscillation will occur; if the learning rate is too small, it will increase the training time, reduce the convergence ability of the network, and the iterative effect is not ideal. In the future, the remaining life prediction method of sand belt based on the improved BP neural network or other deep learning algorithm can be studied to further improve the prediction accuracy and stability.  
Methodology, Y.C.; conceptualization, J.Z.; formal analysis, X.Q.; writing--original draft preparation, B.L.; writing--review and editing, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51135006.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data also forms part of an ongoing study.  
The authors declare no conflict of interest.