# EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism
## 4 Experimental Results  
Experimental equipment and the control process are shown in Figure 6. The primary facility was a 140-ton \(EIMM\), driven by \(SS\). Data analysis was done in a Dell laptop which had a clock speed of 1.7 GHz and 4 GB DDR3 RAM. The controller was KEBA CP052 implemented the Ethercat protocol, and it used 24 v to control input and output. The servo drive was a Phase AXN 70.140.4, and the motor was a Phase U31013F.15.3 N7, whose maximum speed was 3000 RPM. An encoder built in the motor was used to feed back the position. In Figure 6, an experiment with a disposable cup is shown, which verifies the effectiveness of the method intuitively. Table 1 shows the parameters of the \(TM\), \(SS\), and \(EKF\) in the experiment. Based on those parameters, the following experiments were done.  
Figure 5: Process of the self-adaptive method.  
Figure 6: Experimental facility, control process, and effect of \(MP\).  
In our experiment, the s-curve was chosen as the start and stop slope. We set the speed and acceleration of mold open and mold close to 500 mm/s and 2 m/s\({}^{2}\), which are very fast times in real production. The displacement was set to 240 mm. In the section of \(EKF\), the setting profile was chosen as the prediction value, and the drive feedback current was chosen as the measurement value.  
Figure 7 shows the displacement, velocity, acceleration, and force on slider C, which were calculated from the setting profile.  
When using \(EKF\), the \(r\) was 0.01, \(q\) was 0.02, and protection monitor displacement was 7 mm. Then we tested the first effect of \(EKF\), and Figure 8 is the effect of current profile filtered by \(EKF\). Plus icons are measurement values. The green profile is the setting value after being calculated by our mathematical model. The blue profile is the optimized estimated value.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(m_{2},m_{3},m_{5}\) (kg) & 25, 30, 2.5 \\ \(m_{B},m_{C}\) (kg) & 80, 5 \\ \(r_{1},r_{2},r_{3}\) (m) & 0.145, 0.07, 0.190 \\ \(r_{4},r_{5}\) (m) & 0.1, 0.055 \\ \(h,f\) (m) & 0.08, 0.025 \\ \(\phi\) (rad) & 0.4499 \\ \(K_{t}\) (Nm/A) & 0.621 \\ \(l\) (mm) & 40 \\ \(\eta\) & 0.873 \\ \(q\) & 0.01 \\ \(r\) & 0.02 \\ \(Q\) & \(1\times 10^{-4}\) \\ \(R\) & \(4\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the \(TM\), \(SS\), and \(EKF\) in the experiment.  
Figure 7: (**a–d**) The displacement, velocity, acceleration, and force on slider C, respectively, which all change over time.  
In self-adaptive part, we set the learning time to 50. Figure 9 shows the result. The blue profiles are the 50 curves filtered by \(EKF\). The red profile is the upper deviation curve, and the purple one is the lower deviation curve.  
In the next step, we took three experiments. In these experiments, we put three foreign bodies of different thicknesses into a mold individually, whose thicknesses (\(D_{\alpha}\)) were 1, 0.8, and 0.5 mm, respectively. Figure 10 shows the results. In case of 1 mm, it checked out the thickness was within a 0.16 mm range after touching the foreign body; i.e., \(D_{\beta}\) = 0.16. In cases of 0.8 mm and 0.5 mm, the \(D_{\beta}\)s were 0.12 mm and 0.10 mm, respectively.  
Figure 8: The effect of a current profile filtered by \(EKF\).  
Figure 10: Current profiles when the mold closes with foreign bodies of different thicknesses.  
Figure 9: Result of self-adaptive mold protection method. The blue profiles are the 50 curves filtered by \(EKF\). The red profile is upper deviation curve, and the purple one is the lower deviation curve.  
In the last part, we compared precision of the proposed \(EMP\) and general methods. In the contrast experiment, the thickness of each foreign body was arbitrary, and we tested the proposed method and the general one 100 times respectively. Figure 11 shows the results. In these 100 experiments, the average \(D_{\beta}\) of proposed method was 0.94 mm, while the general one was 1.21 mm, which is a decrease of 22%.