# Intelligent Monitoring and Compensation between EDM and ECM
## 3 Case Results  
### Ecm  
ECM is a chemical reaction that alters the energy of the electrons on the electrode surface by influencing electric level to transfer electrons between the conductive workpiece and electrodes. Moving electrons from workpiece to electrode is the result of an oxidation reaction, with the electrode serving as the anode. In ECM (Figure 2a), the feed rate is a crucial factor for changing the size [6,7]. A slower feed speed at a single position may lead to excessive erosion of the chemical liquid. This study established the effect of different sizes, designs, and experimental parameters, as presented as Table 1. Constant voltage was used to alter the feed rate, and we collected the spindle voltage and current for the subsequent step of analysis. The method involved moving the Y axis for processing, as presented in Figure 2b. After five experiments, the workpiece was measured using the coordinate-measuring machine (CMM), as illustrated in Figure 3a, and measured according to its machining direction, as presented in Figure 3b. The measurement results are summarized in Table 2. Because \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Voltage (V)**} & \multicolumn{2}{c}{**Machining Depth**} & \multirow{2}{*}{**Feed Rate (\(\mu\)m/s)**} \\  & & **(mm)** & \\ \hline
**1** & 18 & 10 & 7 \\
**2** & 18 & 10 & 9 \\
**3** & 18 & 10 & 11 \\
**4** & 18 & 10 & 13 \\
**5** & 18 & 10 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ECM Experiment. Figure 2: (a) ECM processing; (b) Machine schematic. An electrical machining (ECM and EDM) matching method was developed to maintain the machining accuracy of the approximate machining process and the postprocessing finishing process, shorten the process connection processing time, analyze the correlations across the process, and calculate the electrical signal and the removal area. The mechanism achieved process matching. Heat map analysis was performed with voltage, current, and postprocessing measurements. The correlation between each variable was determined by assigning a score from \(-1\) to \(1\). Figure 6a presents the current versus area. A high correlation was observed between current. A pair plot was used to analyze the distribution relationship between the two methods, indicating all the distributions between each variable. Figure 6b demonstrates that the relationship between current and size was linear (green color frame) and that between voltage and size (red color frame) was clustered. In application, voltage and current are regarded as input data, and the removed area (dimension) is regarded as output to establish a linear regression model, as presented in Figure 7; to estimate the processing quality, regression analysis is used to determine the relationship between the reaction variable Y and the explanatory variables current and voltage. Figure 5: Voltage value of the five experiments of ECM. Figure 6: (a) Heat map analysis of ECM parameters; (b) distribution analysis of ECM parameter. The total number of samples was 200, and the training model was randomly cut at 90% (180) and 10% (20) for data verification. The mean absolute error (MAE) was used to evaluate the difference between the regression results and the experimental results. If no difference between the two was evident, the MAE was 0. As presented in Figure 8, the MAE of this model reached 0.308 mm\({}^{2}\). To compare the prediction and experimental results, six points were extracted proportionally. The statistical table in Figure 9 shows the actual measurements and predictions, and the error between single-level points and points is expressed as the MAE. The minimum error of No. 2 MAE is 1.856 mm\({}^{2}\). According to the single-point error results in Table 3, the minimum error of a single point is No. 5, the bottom six level of 0.02 mm\({}^{2}\). The resulting error was within the acceptable range.