# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
## 2 Materials and Methods
### Framework  
The case study focuses on the manufacture of fillet welds on steel plates, using two welding techniques, FCAW and GMAW. Thus, the FCAW and GMAW welding processeswere configured on a FANUC LR Mate 200 iD 7L robotic arm (Fanuc, Japan), Figure 3a. The apparatus utilized in this experiment is a Lincoln R450 CE welding system (Lincoln Electric, Cleveland, OH, USA), as illustrated in Figure 3b. This system is equipped with the necessary wire and gas to achieve a precise weld, in accordance with the established processes. The shield gas combination used in the manufacture of the weld seams was composed of 80% argon and 20% carbon dioxide for the GMAW welding process and 75% argon and 25% carbon dioxide for the FCAW process. This combination of gases was employed with the objective of combining the optimal performance of each gas in a welding process, namely CO2 (enhanced penetration) and argon (improved protection). This approach was informed by the specific characteristics of the material to be welded (carbon steel). Furthermore, the reliability of the weld and its final quality are more balanced. This is evidenced by the following observations in [29].  
The SC-420 MC titania flux-cored wire is suitable for all-position welding with either 100% CO2 shielding gas or Ar-20-25% CO2 shielding gas. The reduced sputtering and enhanced slag detachability result in a shorter bead grinding operation [30].  
On the other hand, the SM-70 eco wire, a general-purpose AWS ER70S-6 copper-clad solid wire suitable for manual and semi-automatic applications, was utilized in this investigation for GMAW weld seams. Its extensive utilization in a multitude of industrial sectors, including structural fabrication, automotive, heavy machinery, and shipbuilding, attests to its versatility and reliability [31]. Moreover, the steel employed in the experiments was 6 mm thick carbon steel, designated S275 JR [32]. Weld beads of approximately 5-7 cm were created by L-shaped fillet welding, as can be seen in Figure 4, where five weld beads can be seen. The welding wires and steel utilized in this process are standard materials commonly employed in the shipbuilding industry.  
Figure 3: On the left side, Fanuc 200i-D 7L robotic arm equipped with a welding torch. In the background of this image, you can see the gas bottles (Argon/Carbon Dioxide) in their conveniently mixed proportions. On the right side is the Lilcoln R450 CE Multi-Process Welding Machine, placed under the table of the robotic arm and connected to it.  
Subsequently, each seam was numbered and labeled as acceptable or not acceptable. Furthermore, within the non-acceptable option, its manufacturing defect was labeled. The labels were assigned by two human welding experts, who inspected each seam visually, as would be done in typical surface inspection welding field work.  
The subsequent stage of the study will entail the acquisition of a series of images of the aforementioned welds, with the objective of training a neural network.Once this network has been trained, a single-step Deep Learning architecture, YOLOv8, will be used to predict the type of weld performed and the welding defects found. Thus, the pictures have been taken in different tones of light, aided by the luminaire that appears in Figure 4(a). The process of image capture and its associated positioning was automated by means of a high-resolution camera placed at the end effector of a robotic arm, as illustrated in Figure 4(b).  
Figure 4: Steel plate where numbered seams were welded and then treated according to the experiment to be carried out.  
Figure 5: Equipment utilized for the capture of images in various positions and luminosities included a high-precision camera affixed to the end effector of the robotic arm and a luminaire positioned in different locations, contingent upon the intended image, with the objective of attaining a series of images exhibiting the most diverse range of luminosities feasible, thereby facilitating a more comprehensive training experience.