## 4 Conclusions  
To deal with the issue of metal alloys classification with similar components, a discrete wavelet transform-based metal material analysis model by CPA-PEC method is proposed in this paper, and performances are verified by a series of experiments. The CPA-PEC sensor is excited by a constant phase angle pulse eddy current, and the eddy current signal is analyzed by the discrete wavelet transform. Compared with conventional sine wave excitation, constant phase angle pulse eddy current has a wider spectrum, which may have benefit for spectrum measurement of different metals. Therefore, the proposed method has fast discrimination capability for various metal materials and has good discrimination capability for comprehensive evaluation and quantitative analysis of the subtle differences in metal composition through the discrete signal feature analysis model. Experimental investigations show that the CPA-PEC sensor with metal eddy current feature library and metal material feature identification algorithm can effectively improve the capability of eddy current sensor to identify metal material consistency in application environments such as qualitative determination of special metal material components and dynamic monitoring of metal consistency. The proposed evaluation index can accurately discriminate metals. The identification rates are 100% for all the metals, the stability is greater than 97.10% for all metals, and the relative errors are less than 0.32% for all metals. Compared with other metal identification techniques, such as spectral analysis, the CPA-PEC technique can quickly determine the metal material characteristic index by a simple detection device and achieve higher accuracy and a wider range of metal identification by continuously expanding the eddy current characteristic library. This has higher practicality and a wide application value in the application scenario of rapid nondestructive testing of metal material composition. However, an obvious limitation exists: the CPA-PEC technique is more suitable for qualitative analysis of alloy components, such as measurement of the change in the content of a component in the production of an alloy. In the future, the influence of different components on the electromagnetic properties of the alloy should be fully analyzed, more data should be expanded to the metal eddy current feature library by collecting the experimental signals from more types of the samples, and the performance of the proposed metal material feature identification algorithm should be verified by the different metal categories. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Identification Rate * & **Stability * & **Relative Error * \\ \hline K162 & 100\% & 98.80\% & 0.03\% \\ K163 & 100\% & 98.90\% & 0.03\% \\ K240 & 100\% & 99.12\% & 0.03\% \\ Fe & 100\% & 97.10\% & 0.31\% \\ Air & 100\% & 97.45\% & 0.32\% \\ Cu & 100\% & 98.11\% & 0.03\% \\ Al & 100\% & 98.10\% & 0.12\% \\ St & 100\% & 97.45\% & 0.03\% \\ \hline \hline \end{tabular}  
* Identification rate = (identified samples/total samples) \(\times\) 100%.
* Stability is the samples that were within the confidence interval.
* Relative error = (1 \(\mathrm{EVA_{metal}}-\) mean(\(\mathrm{EVA_{metal}}\))\({}^{1}\)/max(\(\mathrm{EVA_{metal}}\))) \(\times\) 100%.  
\end{table} 
Table 4: The identification rate of the sensor, the stability of the calculation model and the accuracy of the evaluation index. **Author Contributions:** Conceptualization, Y.Y. ; Formal analysis, Y.X. ; Investigation, L.L. ; Methodology, Y.X. and Y.Y. ; Software, L.L. ; Validation, Y.X. and L.L. ; Writing--original draft, Y.X. and L.L. ; Writing--review & editing, Y.Y. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Nature Science Foundation of China [grant numbers 52275522 and 61960206010], the Sichuan Science and Technology Program [grant number 2022 NSFSC032], and the Open Fund of Key Laboratory of Traction Power [grant number TPL2206].