Ramon Helwing

Residual stresses have a significant impact on the quality and service life of brazed components. Therefore, a comprehensive understanding and reliable characterization of the residual stress state in brazed joints are essential. X‑ray diffraction (XRD) is regarded as the conventional method for residual stress analysis. However, its application requires specific safety regulations and qualified staff to ensure adequate radiation protection. These requirements make the procedure comparatively complex, time‑consuming, and costly. In this study, two micromagnetic methods, eddy current (EC) testing and magnetic Barkhausen noise (MBN) analysis, were investigated as potential methods for residual stress characterization. Both techniques are sensitive to residual stresses as well as to other material properties and are comparatively simple and rapid measurements. To evaluate their capabilities and limitations, spatially resolved EC and MBN measurements were performed on a vacuum-brazed tool steel specimen and compared with residual stresses determined by XRD using the cos(α) - method. The focus was on the spatial distribution obtained by the different measurement techniques and on the correlation between the micromagnetic signals and the XRD measurements. The EC and MBN measurements revealed distinct spatial variations across the specimen, while the XRD measurements showed less pronounced variations in the residual stress distribution.

Corrosion causes significant economic losses and safety risks worldwide, making reliable monitoring methods highly relevant in materials science and engineering. Magnetic Barkhausen noise (MBN) is sensitive to changes in surface condition and residual stress and therefore represents a promising non-destructive technique for detecting corrosion-related material degradation. However, the temporal evolution of MBN signals during corrosion has only rarely been investigated. This study presents a robot-assisted experimental setup for automated MBN measurements during cyclic immersion–drying corrosion tests. A collaborative robotic arm handles a corrosion plate and performs a fully automated sequence of imaging, immersion in a 3.5% NaCl solution, drying, and MBN measurement. This approach ensures reproducible handling conditions and enables continuous monitoring of the magnetic response over successive corrosion cycles. The resulting dataset provides spatially and temporally resolved information on corrosion progression. MBN signal characteristics, including RMS value, full width at half maximum, and maximum peak amplitude, are evaluated and correlated with the observed corrosion patterns. The results demonstrate systematic changes in the MBN signal with increasing corrosion and show that characteristic signal variations remain detectable even after removal of the corrosion layer.

Monitoring corrosion in industrial components is essential for ensuring structural integrity, operational safety, and economic efficiency. However, the nonlinear nature of corrosion leads to irregular visual and physical surface changes that conventional image-processing methods struggle to capture. Supervised deep-learning approaches require extensive annotated datasets, often unavailable for diverse material conditions. This study uses the pre-trained CLIP (Contrastive Language–Image Pre-training) model for semantic analysis of nonlinear corrosion progression. By embedding images and descriptive text into a shared latent space, the method detects subtle optical variations, identifies characteristic corrosion features, and derives a directional metric representing progression from initiation to advanced states. Optical analyses of corrosion test plates provide a controlled dataset for evaluating surface evolution and validating the semantic similarity metrics. The results indicate that latent space analysis offers a practical, data-driven approach to automated corrosion monitoring.

In order to achieve realistic simulations of the chip formation, high quality input data regarding the flow stress and damage behavior of the materials are required. The split Hopkinson pressure bar (SHPB) test setup for the characterization of highly dynamic material properties offers a suitable method for generating high strain rates, similar to those in the chip formation zone. However, the strain measurement in SHPB is usually performed by means of strain gauges. This leads to an unreliable evaluation of strain rate and flow stress/shear flow stress in the case of an inhomogeneous material deformation, since this method presents the total strain whilst excluding local deformations. Inhomogeneous deformations are induced deliberately in special shear specimens, as they are also observed in the investigated cylindrical specimens. The present work deals with this issue by providing two additional measurement techniques, which are applied in SHPB tests for cylindrical specimens made of AISI 1045 and Ti6Al4V. To enable a local strain resolution, digital image correlation (DIC) is applied to high-speed images of the deformation process. In order to allow for the detection of shear bands in the specimens, a deep-learning-based approach is presented. The two measurement methods (strain gauges and DIC) are compared and discussed. In particular, the findings regarding the inhomogeneous deformation of Ti6Al4V allow for future improvements in the result quality of SHPB tests. The presented algorithm shows promising predictions for shear band detection and creates the basis for an automated evaluation of shear sample results, as well as an AI-based pre-selection of frames for the DIC evaluation of SHPB tests.