# Deep Neural Networks for Defects Detection in Gas Metal Arc Welding
###### Abstract
Additionally, the temperature and geometry of the arc are correlated with defects, as shown by [PERSON] [11] and [PERSON] [12]. It is not always easy to check for defects in the joints, and  
Figure 2: Self-autoregularization of arc current with constant voltage characteristics. this is a long and time-consuming job, especially when components are standardized and produced in large quantities. A sample check is carried out only on a few random samples in some sectors. In this case, artificial intelligence can be a viable method to evaluate the correct quality level for all components, including the items that skip the inspection stage. This is possible because the process parameters are directly involved in the defects rising, and these are all easily measurable. [PERSON] et al. used different network architectures to correlate current and arc voltage to porosity, with an accuracy between 66-89% [13]. During gas metal arc welding, assuming that the generator has a flat voltage characteristic, slight variations in arc length lead to non-negligible fluctuations in the current affecting the arc: as the arc length increases, the current decreases, and vice versa, as seen in Figure 2.  
In this case, the operator's task--a human or a robotic arm--is to change parameters, such as the torch's height from the workpiece, to bring these values back into range. This self-regulating characteristic of the electric arc leads to variations in current and, therefore, in the specific heat input supplied to the joint, which may cause defects in the items. The Welding Procedure Specification (WPS) is a document that describes welding procedures and contains all the information needed to make quality welds. In the WPS, depending on the workpiece material, type of weld, and thickness, the welding parameters are provided, leading to a specific value for the heat input provided to it. For this work, information regarding process parameters such as current, voltage, and welding speed were collected from welds of different thickness in multi-pass welding, shown in Figure 3.  
These values were compared with those in the WPS, which defines the best process parameters to reach a good quality of welded joint and that is obtained following experiments. The obtained errors were used to train a neural network. To the author's best knowledge, this approach, inspired by control theory, is innovative in this field and helps training and so the performance because the error is a better variable to understand the meaning of the \"high or low\" process parameter, which is correlated to defects in the way presented before. The labels, necessary to train the supervised network, were obtained through non-destructive tests (NDTs), specifically ultrasonic and penetrant liquids tests, and we found the type and location of defects in the workpiece. In particular, ultrasonic testing allows us to find the depth at which a defect can be traced, which is the key to creating the labels for the multi-pass GMAW process. The schematic procedure used for this work is shown in Figure 4.  
Figure 4: Procedure of training used in this work. Figure 3: Multi-pass welding.