# Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
## 5 Conclusions  
This paper researches the problem of identifying defects, mainly burn-through and a lack of fusion, in thin-aluminum-alloy-sheet CMT butt welding based on arc sound signals. The mechanism of CMT arc sound generation is analyzed, as well as its relationship with welding defects. A multi-channel time-frequency convolutional neural network based on an arc sound was constructed to identify the defects of lack of fusion and burn-through in thin aluminum alloy sheet CMT butt welding. This paper also compares the proposed  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Feature** & **Equation** & **No.** & **Feature** & **Equation** \\ \hline
1 & Mean & \(\mu=\frac{1}{n}{\sum\limits_{i=1}^{n}}x_{i}\) & 8 & Root-Mean Square & \(x_{rms}=\sqrt{\frac{\sum_{i=1}^{n}x^{2}}{n}}\) \\ \hline
2 & Mean amplitude & \(x_{arv}=\frac{1}{n}{\sum\limits_{i=1}^{n}}|x_{i}|\) & 9 & Skewness & \(x_{s}=\frac{1}{n}{\sum\limits_{i=1}^{n}}\Big{(}\frac{x_{i}-\mu}{\sigma^{\prime }}\Big{)}^{3}\) \\ \hline
3 & Peak & \(x_{p}=\max\limits_{1\leq i\leq n}|x_{i}|\) & 10 & Kurtosis & \(x_{k}=\frac{1}{n}{\sum\limits_{i=1}^{n}}\Big{(}\frac{x_{i}-\mu}{\sigma^{\prime }}\Big{)}^{4}\) \\ \hline
4 & Peak-Peak & \(x_{pp}=\max\limits_{1\leq i\leq n}x_{i}-\min\limits_{1\leq i\leq n}x_{i}\) & 11 & Kurtosis factor & \(\frac{x_{p}}{\sigma_{rms}}\) \\ \hline
5 & Energy & \(x_{e}=\frac{1}{n}{\sum\limits_{i=1}^{n}}x_{i}^{2}\) & 12 & Pulse factor & \(\frac{x_{p}}{\sigma_{err}}\) \\ \hline
6 & Standard deviation & \(\sigma=\sqrt{\frac{1}{n-1}{\sum\limits_{i=1}^{n}}(x_{i}-\mu)^{2}}\) & 13 & Margin factor & \(\frac{x_{p}}{\Big{(}\frac{1}{4}\sum_{i=1}^{n}\sqrt{|x_{i}|}\Big{)}^{2}}\) \\ \hline
7 & Variance & \(\sigma^{2}\) & 14 & Waveform factor & \(\frac{x_{rms}}{\sigma_{err}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time domain statistical features equations.  
Figure 11: Statistical feature vector construction process.  
Figure 12: Defect recognition result of seven models.  
identification method with methods such as SVM, BPNN and ID3, and obtained the following conclusions.  
1. The CMT arc sound is a timing signal with a highly correlated change in CMT arc energy. The arc energy changes three times during the hot and cold processes alternating in a CMT cycle. Each time the arc energy changes, peak arc sound pulse is produced. With these pulses, the arc sound changes dramatically at the beginning of the peak current.
2. For aluminum alloy thin-plate CMT butt welding defect recognition, especially of the burn-through and lack of fusion defects of thin plates, the multi-channel time-frequency CNN has higher recognition accuracy than does the single-channel time-frequency CNN.
3. The proposed method has better feature extraction capability and higher defect recognition accuracy compared with the traditional classification methods such as SVM, BPNN and ID3 that use manually extracted features as inputs.  
[PERSON] and [PERSON] proposed the idea and were the main contributors involved in writing the manuscript; [PERSON] and [PERSON] provided technical support for the application and training of the algorithms involved in the model; [PERSON] developed a welding data acquisition software; [PERSON] developed a feasible experimental protocol and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (grant numbers 51875072 and 52005071), the Foundation for Overseas Talents Training Project in Liaoning Colleges and Universities (grant number 2018 LNGXGJWPY-YB012), and the Foundation Scientific Research Project in Liaoning Provincial Education Department (grant number IJKMZ2020844).  
Not applicable.  
The data involved in this study are available upon reasonable request from the corresponding author.  
The authors declare no conflict of interest.