# Intelligent Monitoring and Compensation between EDM and ECM
## 3 Case Results
### Edm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**No.1**} & \multirow{2}{*}{**No.2**} & **ASF** & \multirow{2}{*}{**No.3**} & \multirow{2}{*}{**No.4**} & \multirow{2}{*}{**No.5**} \\  & & & & & \\ \hline Average & \multirow{2}{*}{240.273} & \multirow{2}{*}{255.679} & \multirow{2}{*}{126.244} & \multirow{2}{*}{103.03} & \multirow{2}{*}{67.898} \\ value & & & & & \\ \hline \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**No.1**} & \multirow{2}{*}{**No.2**} & \multirow{2}{*}{**No.3**} & \multirow{2}{*}{**No.4**} & \multirow{2}{*}{**No.5**} \\  & & & & & \\ \hline Average & \multirow{2}{*}{1.258} & \multirow{2}{*}{1.156} & \multirow{2}{*}{1.321} & \multirow{2}{*}{1.558} & \multirow{2}{*}{1.828} \\ value & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Averages of the feature values. Figure 14: Comparison of the sixth level after ECM processing and EDM processing. As the contact area decreased, the spark frequency values changed proportionally. This was proportional to the AIDT, and the quantified data of the whole section of machining  
Figure 16: Average ignition delay time. Figure 15: Average spark frequency. results were averaged using calculation features. The model has effective processing features and using a neural network can quickly obtain predictions, such as those presented in Table 5, which correspond to the aforementioned results. To establish a neural network model, the features calculated using EDM were the input, as presented in Figure 17a, to estimate the processing quality. This analysis was used to determine the relationship between the reaction variable Y and the explanatory variables. The maximum training number of the model was 1000. The learning rate was 0.01, and a seven-level neural network was established. In the proposed model, the number of hidden nodes in the proposed model was [10, 4, 1, 10, 7, 10, 7, 4, 1], the total Parameters was 263, as presented in Figure 17b, and the model was established with 90% of the data. The other 10% was used for testing the model. The model establishment result MAE was (1.32 mm\({}^{2}\)), as presented in Figure 17c. The trained model was tested to compare its results with actual values after forecasting. The minimum error of No. 3 MAE is 1.232 mm\({}^{2}\), as presented in Figure 18. The minimum error of No. 3 single point was 0.1 mm\({}^{2}\), and its error result is presented in Table 5. Because the processing easily caused a cone shape, the prediction of level 1 was deemed poor. Figure 17: (a)The configuration of EDM model; (b) model information; (c) The results of predicted and real data of EDM model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **No.1** & **No.2** & **No.3** & **No.4** & **No.5** \\  & **(mm\({}^{2}\))** & **(mm\({}^{2}\))** & **(mm\({}^{2}\))** & **(mm\({}^{2}\))** & **(mm\({}^{2}\))** \\ \hline Level 1 & 11.318 & 29.237 & 28.181 & 24.714 & 19.499 \\ Level 2 & 1.879 & 5.174 & 0.1 & 1.573 & 0.481 \\ Level 3 & 3.765 & 1.582 & 2.316 & 0.3 & 1.473 \\ Level 4 & 1.485 & 1.015 & 0.584 & 2.578 & 1.059 \\ Level 5 & 5.34 & 1.1995 & 0.788 & 4.646 & 2.687 \\ Level 6 & 2.388 & 1.134 & 1.117 & 6.44 & 0.288 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model predictions and actual results error. By combining the sensing collection, signal analysis, feature calculation, and model prediction of the two processes, their respective advantages were preserved, and data could be effectively transmitted to intelligent monitoring to compensate size for differences between EDM and ECM.