## 4 Welding Multi-Source Sensing Systems and Their Information Processing and Knowledge Modeling  
### Multi-Source Information Sensing and Acquisition  
In this paper, for the original robotic welding system, a set of intelligent sensing systems was designed and developed, integrating vibration sensors and molten pool image acquisition, which were mounted on the welding workpiece and the robot end torch, respectively. This system equips the welding production robot with sensing organs similar to eyes and skin, enabling it to sense the surrounding environment in real time. The intelligent sensing system uses high-performance high-speed camera CCDs, vibration sensors, and current Hall sensors to collect real-time images of the welding dynamic molten pool, vibration signals, and current parameters. Through the welding local end controller, multi-source heterogeneous data during the welding process can be collected, displayed, and stored for a short period in real time. In this way, the multi-source sensing system for the arc welding robot can be enhanced to more effectively control the quality, stability, and reliability of the welding process. Figure 4 shows the schematic structure of this intelligent sensing system. This study aims to explore the effects of different GMAW parameters on the welding quality of low-carbon steel and to achieve real-time accurate identification of welding defects using multi-sensor information fusion technology. The experimental specimens were low-carbon steel plates with dimensions of 300 mm \(\times\) 150 mm \(\times\) 6 mm, configured as butt joints in the flat welding position, without using a backing. In the experiments, four parameters--welding current, shielding gas flow rate, welding gap, and welding speed--were set as variables. Multiple experiments were conducted by varying the combinations of these parameters to observe how different settings influence the quality of the weld seam. Each parameter combination was tested three times to ensure data accuracy, with detailed data presented in Table 4.  
Figure 4: Multi-source sensing system. The welding molten pool image is converted by video acquisition and post-processing, with a visual sampling frequency of 10 HZ, and the vibration signal and actual current value are acquired at a frequency of 1000 HZ and analyzed every 100 ms after welding. Adopting the same base material, welding consumables, equipment, and operating environment as that of the ship's hull plate, the simulated parts welding process test is carried out. The test adopted flat plate butt welding using 6 mm thickness Q235 steel, a type of mild steel. The angle of the open bevel is 60\({}^{\circ}\), and the blunt edge measures 1.5 mm. The test simulated the actual situation of ship welding by varying the gap, welding speed, current, and gas flow. Obtained and collated to obtain 01 porosity, 02 incomplete penetration, 03 burnt through, 04 incompletely filled groove, 05 good, 06 weld misalignment a total of six types of quality types of welds of the combination of multi-source information data, the data information is shown in Table 5. Referring to the GB/T 3375-94 [21] welding terminology, to define the corresponding welding defects. Porosity refers to the formation of cavities in the welding process where bubbles in the molten pool fail to escape during solidification and remain. Failure to weld through refers to the phenomenon in which the root of the joint is not completely penetrated during welding. It may also refer to situations where the depth of the weld does not meet the design requirements. Burn-through refers to a defect in which the molten metal flows out of the back of the bevel during the welding process, forming a perforation. Incomplete penetration refers to the formation of continuous or intermittent grooves on the surface of the weld due to insufficient filler metal. Weld deviation refers to the phenomenon that the center line of the weld gap and the center line of the weld toe is not centered.