## 2 Investigated Material Structures: Theoretical Background and Related Works
The authors of [17] presented a two-step convolutional neural network (CNN) system for detecting welding imperfections in galvanized steel, emphasizing the importance of edge detection and noise reduction during preprocessing. The model employed an enhanced Faster R-CNN with Feature Pyramid Networks (FPNs) and a ResNet-101 backbone, outperforming VGG16 in detecting defects of varying sizes. Achieving an AP50 accuracy of 99.21% with edge-detected images, the system reached 99.49% when utilizing edge detection. Despite slight performance drops with Gaussian noise, the model remained robust. This approach highlights the potential of edge detection to improve accuracy and enable real-time weld quality monitoring in industrial settings. The authors of [18] proposed combining pulsed eddy current testing (PECT) with deep learning to non-invasively predict the hardness of bearing rings. Time-frequency data from PECT signals, processed via continuous wavelet transform (CWT), were input into a CNN for automated feature extraction and hardness prediction. With optimized parameters, the model achieved an average prediction error of 1.37% and a maximum error of 1.89%. This approach leverages PECT's contactless data acquisition and deep learning's ability to manage complex relationships, offering a practical and precise method for the quality assessment of bearing rings in manufacturing. The authors of [19] used an ANN to predict the mechanical properties of 304 stainless steel in nuclear reactors. Samples exposed to sensitization temperatures underwent ball indentation testing (BIT) to measure mechanical properties. Microstructural data, including grain size and precipitates, were inputs for an ANN model, achieving accurate predictions. This method offers a non-destructive approach for monitoring 304 SS components in nuclear reactors, avoiding harmful testing. The authors of [20] proposed a method for identifying defect depths in metal sheets using a four-coil eddy current sensor and an enhanced ResNet18 model with dilated convolutions. To address \"lift-off\" interference, the sensor setup was optimized to reduce errors. The model achieved classification accuracies of 98.1% for aluminium and 95.4% for stainless steel, demonstrating fast training and prediction capabilities. Experimental tests confirmed its ability to accurately classify defect depths, orientations, and lift-off effects,achieving 97% accuracy. This approach significantly advances non-destructive testing (NDT) applications. The authors of [21] reviewed advanced sensor technologies for non-destructive testing and structural health monitoring (SHM) in municipal infrastructure. This research article emphasized machine learning's role in improving data processing, anomaly detection, and damage identification precision. Techniques like CNNs are applied in ultrasonic signal analysis and thermography. The study also highlighted IoT integration, sensor miniaturization, and advancements in signal processing and next-generation sensor materials to enhance resilience against environmental factors. The authors of [22] focused on identifying early-stage fatigue degradation in AISI 347 using eddy current (EC) probes and machine learning. They combined EC signals with two methods: StrainLife, based on empirical strain relations, and IntelligentLife, a machine learning approach to estimating fatigue life. Both methods provided similar results, with the ANN model delivering reliable and fast predictions. The study demonstrated the potential of EC testing with machine learning for non-destructive, real-time fatigue monitoring, offering valuable insights for critical industries. Further research could refine predictions by exploring correlations between microstructural changes and EC signals. The main common denominator of all the studies is the drive for automation and improved accuracy in assessing material properties and integrity. In terms of common aspects, the focus is on the use of artificial intelligence, with an emphasis on non-invasive testing using available technologies.