# NDT Method for Line Laser Welding Based on Deep Learning and One-Dimensional Time-Series Data
## 4 Conclusions  
This paper presents a method for analyzing the weld center trajectory data of original gray structured-light images collected with a laser profile sensor device as one-dimensional time-series data. Taking all the defect features of the weld into consideration and adopting a one-dimensional signal processing method, we encoded the two-dimensional time-series images as one-dimensional time series and applied four deep learning neural network models--LeNet, AlexNet, Resnet-50 and VGG-16--to classify and detect four common types of welding defect classifications: burr, concavity, porosity and non-defect. We propose a non-destructive testing method based on linear structured light for encoding one-dimensional weld data, which, compared to the latest non-destructive testing technology, has unparalleled advantages. It has characteristics including fast and efficient detection performance, easy operation, high stability, low costs and excellent robustness. The method is based on high-precision structured-light non-destructive testing equipment, and the detection accuracy was found to be greatly improved by the dimensional expansion and by giving full play to the excellent performance of the CNN network for two-dimensional images. Experiments showed that the method for welding seam non-destructive testing utilizing encoding of high-precision one-dimensional weld seam data as two-dimensional time-series images and combining the advantages of deep neural network was very effective. This validated our hypothesis regarding the power of the combination of defect  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**IMAGE TYPE** & **LENET-5** & \multicolumn{2}{c}{**ALEXNET**} & \multicolumn{2}{c}{**VGG-16**} & \multicolumn{2}{c}{**RESNET-50**} \\ \hline  & **TIME** & **SD2** & **TIME** & **SD2** & **TIME** & **SD2** & **TIME** & **SD2** \\ \hline BMP & 56 min & 0.17664 & 1 h 43 min & 0.17077 & 3 h 44 min & 0.21491 & 7 h 17 min & 0.18710 \\ GASF & 51 min & 0.07115 & 1 h 48 min & 0.10542 & 3 h 35 min & 0.17919 & 7 h 20 min & 0.10612 \\ GADF & 54 min & 0.07082 & 1 h 41 min & 0.09592 & 3 h 42 min & 0.16863 & 7 h 11 min & 0.10551 \\ MTF & 53 min & 0.16926 & 1 h 52 min & 0.13571 & 3 h 39 min & 0.18413 & 7 h 23 min & 0.14390 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The network model inference time (TIME) and loss curve standard deviation (SD2) for the four different types of images with four different neural networks.  
dimension expansion and deep learning. Among the four deep learning neural network models, the VGG-16 neural network model achieved the highest accuracy of 99.60%. The classification accuracy for the two-dimensional time-series images respectively related to the four defect types was significantly improved, with the overall accuracy being about 4% to 6% higher than that of the traditional structured-light images.  
In future research work, we plan to attempt cross-modal fusion of one-dimensional and two-dimensional features by building on existing research. In addition, we suspect that this method may be applicable not only to one-dimensional weld data but also to the one-dimensional ultrasonic data used in other recent NDT technologies, thus making it possible to achieve better detection of deep defects.