# Real-Time Penetration Recognition Based on Voltage Signal in K-TIG Welding
## 3 Experimental Results and Discussion
### Extraction of Characteristic Signal Intensity  
In this section, the paper explores the correlation between the welding melt state and the characteristic signal strength in welding. To extract the characteristic signal strength of the frequency range of 36-37 kHz during the welding process, the paper takes the maximum value of the two similar frequency signal strengths of 36,133 Hz and 36,310 Hz, whose relative sizes are not fixed. Real-time monitoring of the welding melt state was achieved by selecting a data point as a sampling window and performing a fast Fourier transform in a sliding and stepping manner to calculate the characteristic signal strength of the frequency range of 36-37 kHz. The selection of the number of data points and the frequency of calculation are critical factors to consider. If the number of points is too small, the calculated characteristic signal amplitude by fast Fourier transform will be distorted, leading to incorrect trend reflection of the characteristic signal amplitude changes during the welding process. However, selecting too many data points will prolong the calculation time for the fast Fourier transform, slowing down the program's speed and rendering it incapable of meeting the real-time requirements. Figure 3 displays the monitoring of characteristic signal intensity under different sizes of data windows during the welding process: a welding current of 500 A and a welding speed of 240 mm/min. It can be observed that the characteristic signal intensity calculated from a small data window had difficulty in displaying the variation of signal intensity throughout the welding process. Selecting a large window not only increases the computational load of the fast Fourier transform and prolongs the calculation time but also reduces the sensitivity of signal monitoring to some extent. Therefore, in this study, a data window of 1 million data points was chosen, which calculates 5 s of data points simultaneously to select the data window size.  
The sliding step size plays a crucial role in the real-time monitoring of the welding process, as it affects the response rate of signal tracking and the closed-loop control capability of the system. In practical applications, a smaller step size is welcomed, enabling the system to capture welding defect signals and initiate prompt corrective measures. Figure 4 displays the monitoring of characteristic signal intensity under different step sizes during the welding process while choosing a window size of 1 million. It proves that a smaller step window did not cause significant changes in the overall morphology of signal monitoring. Therefore, it is only necessary to ensure that the frequency of the step is less than the time required for the fast Fourier transform of the window data, and once again, the smaller the frequency of the step, the better. However, reducing the step size is constrained by the calculation time required for the fast Fourier transform on the sampling window.  
After extensive analysis, a window size of 1 million data points and a step size of 100  
Figure 3: Feature spectrum monitoring images under different data window size: (**a**) 100 k, (**b**) 200 k, (**c**) 500 k, (**d**) 1 million, (**e**) 2 million.  
Figure 4: Feature spectrum monitoring images under different data step size: (**a**) 10 k, (**b**) 30 k, (**c**) 100 k, (**d**) 300 k, (**e**) 1 million.