# Deep Neural Networks for Defects Detection in Gas Metal Arc Welding
## 4 Conclusions and Future Developments  
In this work, the welding parameters corresponding to each pass during multi-pass GMAW welding of offshore platform bases performed by a robotic axis were monitored.  
\begin{table}
\begin{tabular}{c c} \hline Final validation loss & 0.13 \\ Final training loss & 0.15 \\ Test accuracy & 94.7\% \\ Inference time (ms) & 93.9 \\ \hline \end{tabular}
\end{table}
Table 4: Principal results of Architecture B.  
Figure 19: Loss trend for Architecture B.  
Figure 20: Confusion matrix of Architecture B.  
Subsequently, through non-destructive testing, it was possible to label the welds that did not show any defect, using different NDTs to find defects of different topologies. Once the dataset was generated, it was followed by a pre-processing activity, which led to obtaining four characteristic deviations of the welding parameters, acquired from the process with a sample rate of 225 ms, from those defined by the Welding Procedure Specification. With these deviations, a neural network with one or two hidden layers was trained, and the results were compared. At the end of the training phase, the neural network B presented 97% accuracy for joint classification. The proposed method is a novelty because is not only based on the usual data-driven approach of deep neural networks but is inspired by control theory with the concept of error. This new approach guarantees better results compared to other works, like that of [PERSON] et al. [13], who proposed a similar approach with an accuracy of less than 90% for porosity detection. Furthermore, this network is also trained on different kinds of defects. These results represent a starting point for developing a more robust online quality control system for GMAW, which will be developed by integrating a camera in the inspection loop, which will allow the detection of other defects, including geometric ones during the welding process. However, in this work, only one type of material and wire were considered and is based on one type of WPS. To guarantee a generalization to any GMAW process more experiments must be conducted because the same error should not have the same influence on different materials, so other inputs could be considered, such as wire and workpiece material, joint temperature, gas flow rate, etc. The next step of this work could be the integration of other information like images and new experiments that lead to improving the performance and generalization capability of the network. After this, it will be possible to use the results in real-time for various purposes, such as the development of a new control logic for the feeder unit and robotic axis to adjust the parameters with the aim to avoid weld defects, which can work in the same loop with an intelligent monitoring system.  
Data curation, L.N., G.M. and M.V.; Formal analysis, L.N. and G.M.; Investigation, G.M.; Methodology, G.M. and M.V.; Software, G.M. and M.V.; Supervision, L.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData are available under request to corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
WPS Welding Procedure Specification  
GMAW Gas Metal Arc Welding  
ANN Artificial Neural Networks