# Research on the Predictive Algorithm of Wear Degree Based on Adaptive Feature Learning
## 3 Experimental Design and Verification
### Experimental Results and Analysis
This indicates that neural network models may suffer from reduced sensitivity to changes in input signals and decreased prediction accuracy due to their nonlinear characteristics. The combination prediction model optimizes performance by incorporating linear prediction components, achieving the goal of improving wear degree prediction. The experimental results demonstrate its effectiveness. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **LSTM** & **GRU** & **SAEs** & **DA-Encoder-Decoder** & **DA + ARIMA** \\ \hline MAE & 4.2118 & 4.2412 & 5.5647 & 3.2247 & 3.1658 \\ RMSE & 6.5832 & 6.6207 & 8.1053 & 3.7151 & 3.5920 \\ MAPE (\%) & 13.4629 & 13.5573 & 15.6877 & 9.4890 & 9.2382 \\ R\({}^{2}\) & 0.8264 & 0.8186 & 0.8025 & 0.8751 & 0.8895 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of performance between combined prediction models and other basic algorithms. Figure 7: Comparison of algorithms with fusion of different attention mechanisms. Experiment 3: Comparative experiments on the publicly available NASA dataset. From Experiments 1 and 2, it can be observed that the proposed wear prediction method in this paper outperforms other baseline models significantly. This demonstrates the effectiveness of the trained model for wear prediction proposed in this study. Additionally, considering the limitation of a single dataset, in Experiment 3, the publicly available NASA milling tool wear data was used to predict tool wear under different conditions and parameters, further validating the effectiveness of the proposed method and confirming the conclusions drawn from the analysis. Furthermore, a comparative analysis was conducted between the algorithm studied in this paper and recent works on wear prediction, aiming to validate the universality and robustness of the algorithm. In this section, the APCANet-MP-SVR (Activated PCANet with max pooling and support vector regression) model [31] and the SE-IDANN (sample expansion and improved domain adversarial training of neural networks) model [32] were used as comparative models. The APCANet-MP-SVR model improves tool wear prediction accuracy by optimizing the principal component analysis model (PCANet). The SE-IDANN model addresses the challenge of weak tool wear features by enhancing the domain adversarial neural network (DANN), enabling accurate prediction of tool wear quantity. The fitting curves of the comparative experiments on the NASA milling tool wear dataset are shown in Figure 10, and the results of the comparative experiments are presented in Table 5.  
Figure 8: Wear prediction results of LSTM, GRU, and SAEs. Figure 9: Wear prediction results of the combination prediction model with added linear autoregressive component. From the data in Table 5, the R\({}^{2}\) values (used to measure the goodness of fit, where a value closer to 1 indicates better model performance) for different models on the dataset are all above 0.85, and the models demonstrated relatively stable accuracy in prediction. The DA + ARIMA model outperforms the APCANet-MP-SVR model and the SE-IDANN model in all three different test cases. The DA + ARIMA model achieves a 4.05%, 2.14%, and 4.02% higher R\({}^{2}\) value than the APCANet-MP-SVR model and a 4.62%, 3.42%, and 4.52% higher R\({}^{2}\) value than the SE-IDANN model in the respective test cases. Additionally, it can be observed that the DA-Encoder-Decoder model also exhibits slightly higher prediction performance compared to the two comparative models. The comparison metrics listed in Table 5 further validate the performance of the proposed model in this study, indicating its robustness.