# Deep Neural Networks for Defects Detection in Gas Metal Arc Welding
###### Abstract  
Welding is one of the most complex industrial processes because it is challenging to model, control, and inspect. In particular, the quality inspection process is critical because it is a complex and time-consuming activity. This research aims to propose a system of online inspection of the quality of the welded items with gas metal arc welding (GMAW) technology through the use of neural networks to speed up the inspection process. In particular, following experimental tests, the deviations of the welding parameters--such as current, voltage, and welding speed--from the Welding Procedure Specification was used to train a fully connected deep neural network, once labels have been obtained for each weld seam of a multi-pass welding procedure through non-destructive testing, which made it possible to find a correspondence between welding defects (e.g., porosity, lack of penetrations, etc.) and process parameters. The final results have shown an accuracy greater than 93% in defects classification and an inference time of less than 150 ms, which allow us to use this method for real-time purposes. Furthermore in this work networks were trained to reach a smaller false positive rate for the classification task on test data, to reduce the presence of faulty parts among non-defective parts. GMAW; neural networks; quality inspection; artificial intelligence; machine learning Article  
Deep Neural Networks for Defects Detection in Gas Metal Arc Welding [PERSON], Mattera, V  
more significant for multi-pass welding, where the quality of the previous pass affects the next one. Moreover, this results in a productivity problem since repairing a defective joint involves considerable costs in terms of resources and time. Review of GMAW Process and Defects  
The welding process is based on a localized heat source that allows the melting of the materials. The intensity of the heat source determines the thermal transient and the mode of melting and re-solidification of the joint, on which the quality depends. The formula for specific heat input is as follows:  
\[Q=\frac{I*V}{v_{t}}*\eta\quad\left[\frac{\text{J}}{\text{mm}}\right] \tag{1}\]  
The basic parameters to be monitored in a GMAW process are the welding speed \(v_{t}\), the generator voltage \(V\), and the arc-current \(I\). In particular, the arc current is strongly variable with contact to workpiece distance (CTDW), wire feed rate, and wire properties, such as diameter and material [4] since the generator characteristic for this process is generally flat voltage, shown in Figure 2[5]. Defects in welding are principal of two types:  
* Metallurgical discontinuities, which are problematic primarily due to the drop in mechanical properties of the joint and are typically identified through nondestructive testing. * Metallurgical inhomogeneity, which is more complex to identify and assess. Typical defects include cracks, inclusions, and lack of penetration [6; 7]. These defects are associated with unsuitable process parameters and, therefore, unsuitable specific heat. For example, if the specific heat is too low due to low current or high torch speed. In this case, there may be a lack of fusion of the edges to be welded or a lack of penetration, while high values of specific heat, associated with a low torch speed, may lead to the presence of cracks or metallurgical in-homogeneity. An inadequate value of specific heat supplied to the weld bead can also lead to the birth of inclusions, such as gaseous inclusions, which consist of gas entrapment in the weld pool due to too fast cooling associated with too high welding speeds. In the literature, a strong correlation between process parameters and defects is presented. [PERSON] et al. showed the correlation between arc current and porosity [8]. [PERSON] shows that current and voltage, which are correlated with acoustic emissions [9], are correlated with the depth of penetration and geometry aspect of the joint for a P-GMAW [10].