## 5 Conclusions  
This contribution aims to introduce the current state-of-the-art deep learning techniques to detect weld metal and weld defects. A pre-trained neuronal network was trained and optimized according to the initialization of weights, optimization algorithm, backbone and hyperparameters. By pre-processing the data and adjusting the neural network model, a value of 76.88% of the mIoU was finally obtained for the defined classes. The neural network can automatically detect different areas within micrographs. The detection of weld metal has a high reliability, with an IoU of around 95%. Throughout the research process, a high-definition data set containing 282 images was created. It can be applied to any semantic segmentation model, which has significant implications for future research. Further research can continuously improve the neural network model based on the created data set to achieve better process improvement and quality assurance. With the automated detection of certain features within the weld area, it would also be possible to evaluate the quality of the welds. The results from the deep learning-based weld contour and defect detection could be compared with the established standards, such as DIN EN ISO 13919-1:2020-03, and the weld seam could be evaluated accordingly [20]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Segmentation Result** & **Class** & **Predicted Pixel Count** & **Labeled Pixel Count** & **IoU** \\ \hline \multirow{4}{*}{1} & Weld metal & 8.6303 \(\times\) 10\({}^{5}\) & 8.5214 \(\times\) 10\({}^{5}\) & 95.17\% \\  & Background & 1.6287 \(\times\) 10\({}^{6}\) & 1.6501 \(\times\) 10\({}^{6}\) & 97.89\% \\  & Pores & 7329 & 7284 & 25.18\% \\  & Cracks & 43,485 & 51,612 & 52.67\% \\  & Seam shape defects & 1.0602 \(\times\) 10\({}^{5}\) & 87,489 & 79.76\% \\ \hline \multirow{4}{*}{2} & Weld metal & 1.0318 \(\times\) 10\({}^{6}\) & 1.0313 \(\times\) 10\({}^{6}\) & 96.20\% \\  & Background & 1.5617 \(\times\) 10\({}^{6}\) & 1.5646 \(\times\) 10\({}^{6}\) & 98.13\% \\  & Pores & 2033 & 6410 & 15.94\% \\  & Cracks & 70,341 & 70,487 & 67.09\% \\  & Seam shape defects & 92,548 & 85,578 & 84.28\% \\ \hline \multirow{4}{*}{3} & Weld metal & 1.0517 \(\times\) 10\({}^{6}\) & 1.0315 \(\times\) 10\({}^{6}\) & 93.85\% \\  & Background & 1.8082 \(\times\) 10\({}^{6}\) & 1.84 \(\times\) 10\({}^{6}\) & 97.30\% \\ \cline{1-1}  & Pores & 35,269 & 18,796 & 47.90\% \\ \cline{1-1}  & Cracks & 1.0546 \(\times\) 10\({}^{5}\) & 1.0574 \(\times\) 10\({}^{5}\) & 65.57\% \\ \cline{1-1}  & Seam shape defects & 59,646 & 64,209 & 51.59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Numerical values of segmentation results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segmentation Result** & **Pores** & **Cracks** & **Seam Shape Defect** \\ \hline
1 & 0.72\% & 4.26\% & 10.40\% \\
2 & 0.17\% & 5.88\% & 7.73\% \\
3 & 2.82\% & 8.42\% & 4.76\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of parsed results using the ratio of each defect in the weld area. **Author Contributions:** Conceptualization, C.N., T.G., J.G., S.N., J.T., J.H., S.K. and J.W. ; methodology, C.N., T.G. and J.G. ; software, C.N. and T.G. ; validation, C.N., T.G. and J.G. ; formal analysis, [PERSON], T.G. and [PERSON]; investigation, [PERSON], [PERSON] and J.G. ; data curation, [PERSON], T.G., J.G. and S.N. ; writing-original draft, [PERSON] and T.G. ; writing--review and editing, [PERSON], T.G., J.G., [PERSON] and [PERSON]; visualization, [PERSON], T.G. and J.G. ; supervision, [PERSON] and S.K. ; project administration, [PERSON] and J.G. ; funding acquisition, J.W. and S.K. All authors have read and agreed to the published version of the manuscript. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)--CRC 1153, subproject A3--252662854. The authors would like to thank them for the support. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.