## 3 Experiment and Results
### Discrete Wavelet Transform--Based Metal Material Analysis Model
Multiple groups of feature parameters can be combined for simultaneous discrimination, which can improve the metal identification accuracy and expand the identification range. In order to analyze the effectiveness of the described method more intuitively, an evaluation index is proposed and its stability and accuracy are analyzed based on experiments. The evaluation index is defined as  
Figure 3:1  
where \(character_{k}\) is the extracted parameter defined from peak frequency, peak time, zero crossing time, time of decay, main peak area and amplitude of attenuation; \(S_{n}\) is the signal amplitude in time domain; \(m\) is the number of times the experiment was repeated; and \(c_{k}\) is a balanced value for each extracted parameter, which is positively correlated with the score in Figure 3:2  
The model and calculation equation are tested by 1000 independent sampling, from which 100 random samples are used in the accuracy verification. The results are shown in Figure 3:3 It is clear that the eight chosen kinds of metal are all well categorized. The EVA value for each metal is extremely stable, except the first value for air condition. It can be seen that the EVA values of Fe-K162, Fe-K163, Fe-K240 and Fe are very close, which means that iron-based metal materials are more difficult to classify. When using the EVA value in air as the standard, it can be seen that the EVA values of metals with magnetism are bigger than the EVA value of air. The EVA values of metals without magnetism are smaller than the EVA value of air. The sensor, model and calculation equations were tested by 1000 independent samples, among which 100 random samples were selected to verify the identification rate, stability and accuracy. The results are shown in Figure 3:4 It can be seen that the identification rates for all the metals are 100%. The stability of the sensor for all metals classification are greater than 97.10%. The metal material with the least stability during measurement is Fe, and the metal material with the greatest stability during measurement is Fe-K240. The confidence interval has a mean value of \(\pm\) 0.33%. According to the statistics without anomalies, the relative error of the EVA value is less than 0.32%. The minimum relative error is only 0.03%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Repeat 1** & **Repeat 2** & **Repeat 3** & **Repeat 4** & **Repeat 5** \\ \hline K162 & 2966 & 2966 & 2965 & 2965 & 2966 \\ K163 & 2956 & 2956 & 2955 & 2956 & 2955 \\ K240 & 2946 & 2946 & 2945 & 2945 & 2946 \\ Fe & 2951 & 2951 & 2951 & 2951 & 2951 \\ Air & 3080 & 3090 & 3090 & 3090 & 3090 \\ Cu & 3210 & 3210 & 3211 & 3211 & 3210 \\ Al & 3221 & 3221 & 3221 & 3225 & 3221 \\ St & 3125 & 3125 & 3125 & 3126 & 3125 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The quantization index of the model was verified repeatedly with 100 random samples per group. Based on the experimental data validation results, when the excitation parameters are fixed, four kinds of standard metal materials and three kinds of gray cast iron materials of the same type are measured, respectively, and the evaluation indexes of metal eddy current characteristics are calculated. The results can be distinguished one by one. Through 1000 repeated experiments, the recognition rate of the sensor reaches 100%, the stability of the calculation model is more than 97.1%, the comprehensive accuracy error is less than 0.32%, and the average measurement speed is about 50 ms.