# Neighborhood Rough Fuzzy Penetration Control Method with Variable Precision Based on GMAW
## 4 Experimental Verification and Analysis  
In order to simulate the variation of the weld gap during the welding process, three sets of experiments were designed as penetration control objects for MAG welding and the variable precision neighborhood rough-fuzzy control model was validated. The experimental conditions were as follows: plate Q235 steel plate butt joint, plate thickness of 6 mm, a groove with a 60\({}^{\circ}\) Y-shape, a reserved dull edge of 1.5 mm, and a welding speed of 47 cm/min. The three sets of experiments are classified according to the weld gap as 1.2-0.6-1.2 mm gap A, 1.2-1.8-1.2 mm gap B, and progressive gap C. D is a schematic of the plate machined grooves, blunt edges, etc. The three sets of experiments classified according to the weld clearance: 1.2-0.6-1.2 mm clearance A, 1.2-1.8-1.2 mm clearance B, and progressive clearance C. D is a schematic of the machined grooves, blunt edges, etc. of the plates. The schematic diagram of specific specimens is shown in Figure 11. For each set of specimens, constant gauge process parameters and parameters after control adjustment were used as welding process parameters. After welding, the back weld width was measured to analyze the quality of the penetration. Figure 11: Diagram of machined specimen. Figure 10: 3D space diagram after fuzzy inference, representing the relationship between \(E\),\(EC\), and \(\Delta I\). Improper assembly or insufficient manual control of clearance accuracy during welding can lead to variations in plate clearance, and the welding itself and equipment parameters are not capable of completing the basic weld molding requirements for some specific extreme weld gaps. For this reason, 0.4 mm was chosen as the minimum encountered gap and 2.0 mm as the maximum encountered gap. Considering that there are errors such as plate assembly, material processing, and algorithm accuracy in the assembly process, the area coefficient \(C_{TS}\) of the molten pool extracted by the algorithm will also change. In Figure 12, Figures 14 and 16, we used three characteristic parameters to describe the changes in the welding process of different specimens, namely, the welding current (a) in the welding process, the area coefficient of the molten pool (b) in the welding process, and the backside weld width (c). As shown in Figure 12, taking the experiment of group A as an example, under constant current, there will be a phenomenon of non-penetration at the gap of 0.6 mm, and the area coefficient \(C_{TS}\) of the molten pool tail changes up and down around 0.71. In the case of an s gap, the phenomenon of non-penetration occurs, and the current is adjusted by the variable precision neighborhood rough fuzzy controller (VPNRS-FC). The non-penetrating part was fully welded, the area coefficient of the weld pool tail was reduced to approximately 0.69, and the actual measured weld back width was controlled to be within the appropriate range. During the welding process, the temperature of the welding area is extremely elevated, and uneven expansion and contraction of the weld and other areas will occur [20]. The coefficient in the tail region changes due to the effect of weld heat accumulation, and the penetration effect deepens throughout the weld. The whole welding adjustment process was adjusted twice to take into account the effect of welding heat accumulation. For the first time, in order to enable rapid changes in the weld pool, a method beyond current adjustment was used to achieve faster penetration. Based on the first adjustment, the fine-tuning is carried out a second time to reduce the current adjustment. The control effect of the controller in the experimental welding process of group A is shown in Figure 13.  
As shown in Figure 14, the welding experiment of group B is adjusted by the variable precision neighborhood rough-fuzzy controller.