## 1 Introduction  
The weld quality has a direct impact on the performance and lifespan of welded components. Weld defects reduce the weld quality and deteriorate its properties, which is why they should be avoided. There are many approaches to reduce the occurrence of weld defects such as cracks, pores, lack of fusion or incomplete penetration. Therefore, an effective and efficient method of detection and analysis of weld defects is an important topic. To study the physical structure of metals, metallography is typically used for this purpose. After the preparation of the specimens, an expert has to identify the weld seam's properties qualitatively. This evaluation of welds is done manually with the aid of software. The area is marked with lines, whereupon the program specifies it in accordance with the scale. This procedure is time-consuming, and the result depends on the operator. The software cannot reliably distinguish between cracks, pores and other microstructures. It looks for transitions from light to dark areas depending on the set limit, and it can only indicate the roundness of dark particles in bright unetched matrix. For better analysis and comparison between different welds, it is beneficial to describe the weld properties as scalar quantities. However, since the artificial intelligence is being applied more and more in research and industry, quantized values are needed, especially for machine learning. By determining the ratio of significant areas within a micrograph, the effects of parameter changes can be investigated better and compared to each other.  
Researchers are gradually applying artificial intelligence to the field of laser welding, such as using the quality inspection system to achieve non-destructive weld measurement and defect detection [1], in-process monitoring based on deep learning [2] or using the semantic segmentation algorithm to detect weld defects in safety vents of power batteries [3]. [PERSON] et al. [4] opened the era of fully convolutional networks (FCN) for semantic segmentation. There are currently many variants of FCN-based models that have contributed to the exploration of semantic segmentation [5; 6; 7; 8; 9]. [PERSON] et al. provide an overview of the use of artificial intelligence in welding technology [10]. [PERSON] describes a method of defect detection using a synthetic image dataset if a large number of input images on convolutional neural networks is not accessible [11].  
Due to the non-uniformity of the shape, position and size of weld defects, it is a complicated task to manually analyse and evaluate the recorded weld defect patterns. For example, cracks are sometimes wide and sometimes narrow, and there is no fixed standard for length. The pore is not always an ideal circle, and sometimes cracks and pores are connected. These are some of the difficulties in artificially distinguishing the types of weld defects. There are cases in which it is difficult to identify weld defects, such as identifying the boundary of the weld area from the fuzzy heat-affected zone. At the same time, a fair quantitative analysis is also particularly important for the evaluation of welding performance. Therefore, human participation is still always necessary for the analysis of complex weld defects. Hence, it is a very time-consuming and therefore expensive task.  
This contribution provides a weld contour and weld defect identification and analysis based on a self-created data set. This leads to convenience for further research in the future. This preliminary work is used to create datasets suitable for processing with machine learning algorithms. From these, predictions can be made about weld quality under different parameter configurations. While many AI-based methods focus on real-time data during the welding process, the deep learning-based detection of structures in the micrographs simplifies the link between real-time data recorded during the welding process and the micrographs evaluated afterwards.