## 4 Welding Multi-Source Sensing Systems and Their Information Processing and Knowledge Modeling
### Melt Pool Image Analysis and Feature Extraction  
The literature in [22] mentions a close relationship between weld quality and melt pool image features. Therefore, this paper presents melt pool images of porosity, incomplete penetration, burned-through, under-welded, good, and weld deviation. These images and their corresponding welds, as shown in Figure 5, were obtained by building a welding platform and designing experiments to simulate ship welding. From the physical weld images, it can be seen that each defect has a distinct appearance, 01 displays obvious  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Welding Current**} & \multirow{2}{*}{**Main Welding Process**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Number of**} \\  & & & & \\ \cline{4-5} \cline{5-  
porosity; 02 appears similar to other defects from the front view, but the back view clearly shows no melt-through; 03 is clearly distinguishable from others, exhibiting signs of leakage and perforation; 04 shows a minor groove with the weld positioned below the workpiece when viewed from the back; 05 has the weld not below the workpiece from the front view, while the back shows some residual height and width; 06 shares similarities with other weld surfaces, but it can be differentiated as the weld is not centered within the gap. From the molten pool image, the shapes of these states are still somewhat differentiated, where the area of 05 is more round and full here, and the shape of 03 is gourd-like. By counting their appearance characteristics and shape features, the welding defect information system can be effectively enriched to provide a basis for automatic defect recognition. From the grey-level map of different states, the grey-level distribution varies across intervals. Thus, grey-level statistics can serve as a valuable addition to the defect information system. Combined with the analysis and generalization of the above-molten pool image characteristics and dimensional morphology, it is proposed to extract the continuous region with a grey value greater than 50 in the molten pool image as the profile of the molten pool and calculate the geometric dimensional information such as the maximum width of the molten pool, w, and the half-length of the molten pool, LT, under the six states of porosity, under-welded, burned through, under-welded, good, and weld deviation, respectively. The number of pixel points in the statistical interval, in order, is [50, 99] marked as P50, [100, 149] marked as P100, [150, 199] marked as P150, and [200, 255] marked as P200; the grey level map of each quality type is shown in Figure 6.  
Convolutional neural networks (CNN) can effectively capture local and global features in an image through the combination of convolutional, pooling, and fully connected layers [23]. CNN can automatically extract key information such as edges, texture, and higher dimensional image information. In this paper, by constructing a melting pool image detection model of CNN and using the average pooling layer to extract the higher dimensional features of the melting pool image, we finally obtain the CNN of 128 molten pool attributes for combining with features from other information sources to construct an information decision system describing the defect recognition of molten pool images. The CNN processing principle and feature extraction process are shown in Figure 7. In this paper, a CNN for extracting molten pool images is constructed with specific parameters:  
Figure 5: Welding defects and their corresponding melting pools. (**a**) physical drawing of the welded seam; (**b**) defect melt pool diagram. 1. Input layer: accepts a 32 \(\times\) 32 \(\times\) 3 image as input, normalized using 'zerocenter'. 2. First convolution block: * Convolutional Layer 1: Uses 32 3 \(\times\) 3 convolutional kernels in steps of [1, 1] with'same' padding. * Batch Normalization 1: Normalizes 32 channels. * ReLU1 activation function.